基于对数正态自回归（AR）模型的公交行程时间预测
Bus Travel Time Prediction: A Lognormal Auto-Regressive (AR) Modeling Approach
摘要：
在城市地区，提供关于公交车到达时间的实时信息已经成为不可避免的，以使系统比各种其他交通方式更用户友好和有利。然而，在动态变化的交通条件下，特别是在没有车道规则的异质交通条件下，准确预测公交车的到达时间仍然是一个具有挑战性的问题。多年来，研究人员采用的一种广泛方法是将整个公交路线划分为多个部分，并将这些部分的行程时间作为数据输入（来自GPS轨迹）进行预测。本文采用这种方法，并提出了预测建模方法，充分利用时间相关性的巴士GPS数据。具体来说，我们提出了两种方法：（a）经典的时间序列方法采用季节性AR模型（b）非传统的线性，非平稳AR方法。第二种方法是一种新的技术，利用偏相关的概念从数据中学习。对印度条件（此处使用）数据的边缘分布进行详细分析，发现主要为对数正态行为。这方面被纳入上述提出的预测模型和对数正态意义上的统计最优预测方案被用于所有预测。上述两种时间预测建模方法都独立地在每个分段处提前预测。然而，对于实时公交车旅行时间预测，需要在空间上提前预测多个段。为了实现一个完整的解决方案，该研究还提出了一个智能的程序来执行（实时）多节提前旅行时间预测的基础上，上述提出的时间模型。结果表明，使用所提出的方法在预测精度方面有明显的提高，
英文摘要：
Providing real time information about the arrival time of the transit buses has become inevitable in urban areas to make the system more user-friendly and advantageous over various other transportation modes. However, accurate prediction of arrival time of buses is still a challenging problem in dynamically varying traffic conditions especially under heterogeneous traffic condition without lane discipline. One broad approach researchers have adopted over the years is to segment the entire bus route into segments and work with these segment travel times as the data input (from GPS traces) for prediction. This paper adopts this approach and proposes predictive modelling approaches which fully exploit the temporal correlations in the bus GPS data. Specifically, we propose two approaches: (a) classical time-series approach employing a seasonal AR model (b)unconventional linear, non-stationary AR approach. The second approach is a novel technique and exploits the notion of partial correlation for learning from data. A detailed analysis of the marginal distributions of the data from Indian conditions (used here), revealed a predominantly log-normal behavior. This aspect was incorporated into the above proposed predictive models and statistically optimal prediction schemes in the lognormal sense are utilized for all predictions. Both the above temporal predictive modeling approaches predict ahead in time at each segment independently. For real-time bus travel time prediction, one however needs to predict across multiple segments ahead in space. Towards a complete solution, the study also proposes an intelligent procedure to perform (real-time) multi-section ahead travel-time predictions based on either of the above proposed temporal models. Results showed a clear improvement in prediction accuracy using the proposed methods,
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。
