Intersection Traffic Prediction Using Decision Tree Models
Intersection Traffic Prediction Using Decision Tree Models
复制标题
使用决策树模型进行交叉口交通预测
DOI：
10.3390/sym10090386
复制
发表时间：
2018-09-01
期刊：
影响因子：
2.7
通讯作者：
Wang, Yu
中科院分区：
文献类型：
--
作者：
Alajali, Walaa;Zhou, Wei;Wang, Yu
交通预测是智能交通系统的一项重要任务。十字路口的预测具有挑战性，因为它涉及各种参与者，如车辆、骑自行车的人和行人。本文提出了一种新的交叉口交通预测方法，即在预测模型中引入道路交通量数据以外的额外数据源。特别是，我们利用从十字路口附近发生的道路事故和道路工程报告中收集的数据。此外，我们还研究了两种类型的学习方案，即批学习和在线学习。批学习方案采用梯度增强回归树（GBRT）、随机森林（RF）和极端梯度增强树（XGBoost）三种常用的集成决策树模型，在线学习方案采用带漂移检测的快速增量模型树（FIMT-DD）模型。使用澳大利亚维多利亚州政府发布的公共数据集对拟议的方法进行了评估。结果表明，结合附近交通事故和道路工程信息可以提高交叉口交通预测的准确性。
Traffic prediction is a critical task for intelligent transportation systems (ITS). Prediction at intersections is challenging as it involves various participants, such as vehicles, cyclists, and pedestrians. In this paper, we propose a novel approach for the accurate intersection traffic prediction by introducing extra data sources other than road traffic volume data into the prediction model. In particular, we take advantage of the data collected from the reports of road accidents and roadworks happening near the intersections. In addition, we investigate two types of learning schemes, namely batch learning and online learning. Three popular ensemble decision tree models are used in the batch learning scheme, including Gradient Boosting Regression Trees (GBRT), Random Forest (RF) and Extreme Gradient Boosting Trees (XGBoost), while the Fast Incremental Model Trees with Drift Detection (FIMT-DD) model is adopted for the online learning scheme. The proposed approach is evaluated using public data sets released by the Victorian Government of Australia. The results indicate that the accuracy of intersection traffic prediction can be improved by incorporating nearby accidents and roadworks information.