评估机器学习算法用于极端天气期间的近实时公交乘客预测
Assessing Machine Learning Algorithms for Near-Real Time Bus Ridership Prediction During Extreme Weather
摘要：
由于气候越来越不稳定，天气和过境客流量之间的关系引起了越来越多的关注。然而，时空依赖性和非平稳性带来的挑战尚未完全解决在建模和预测过境客流量的影响下的天气条件，特别是与传统的统计方法。利用澳大利亚布里斯班三个月的智能卡数据，本研究采用并评估了一套机器学习算法，即，随机森林、极端梯度增强（XGBoost）和Tweedie XGBoost，用于模拟和预测与天气条件突变有关的近实时公交车乘客量。该研究证实，确实存在一个显着水平的时空变化的天气-乘客量的关系，产生同样的动态模式的预测误差。模型性能的进一步比较表明，Tweedie XGBoost在空间和时间上生成更准确的预测结果方面优于其他两种机器学习算法。未来的研究可能会通过利用更大的数据集并应用更先进的机器和深度学习方法来推进当前的研究，为交通系统的实时运行提供更多增强的证据。
英文摘要：
Given an increasingly volatile climate, the relationship between weather and transit ridership has drawn increasing interest. However, challenges stemming from spatio-temporal dependency and non-stationarity have not been fully addressed in modelling and predicting transit ridership under the influence of weather conditions especially with the traditional statistical approaches. Drawing on three-month smart card data in Brisbane, Australia, this research adopts and assesses a suite of machine-learning algorithms, i.e., random forest, eXtreme Gradient Boosting (XGBoost) and Tweedie XGBoost, to model and predict near real-time bus ridership in relation to sudden change of weather conditions. The study confirms that there indeed exists a significant level of spatio-temporal variability of weather-ridership relationship, which produces equally dynamic patterns of prediction errors. Further comparison of model performance suggests that Tweedie XGBoost outperforms the other two machine-learning algorithms in generating overall more accurate prediction outcomes in space and time. Future research may advance the current study by drawing on larger data sets and applying more advanced machine and deep-learning approaches to provide more enhanced evidence for real-time operation of transit systems.
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。
