The aim of deploying intelligent transportation systems (ITS) is often to help engineers and operators identify traffic congestion. The future of ITS-based traffic management is the prediction of traffic conditions using ubiquitous data sources. There are currently well-developed prediction models for recurrent traffic congestion such as during peak hour. However, there is a need to predict traffic congestion resulting from non-recurring events such as highway lane closures. As agencies begin to understand the value of collecting work zone data, rich data sets will emerge consisting of historical work zone information. In the era of big data, rich mobility data sources are becoming available that enable the application of machine learning to predict mobility for work zones. The purpose of this study is to utilize historical lane closure information with supervised machine learning algorithms to forecast spatio-temporal mobility for future lane closures. Various traffic data sources were collected from 1,160 work zones on Michigan interstates between 2014 and 2017. This study uses probe vehicle data to retrieve a mobility profile for these historical observations, and uses these profiles to apply random forest, XGBoost, and artificial neural network (ANN) classification algorithms. The mobility prediction results showed that the ANN model outperformed the other models by reaching up to 85% accuracy. The objective of this research was to show that machine learning algorithms can be used to capture patterns for non-recurrent traffic congestion even when hourly traffic volume is not available.

部署智能交通系统（ITS）的目的通常是帮助工程师和操作员识别交通拥堵。基于ITS的流量管理的未来是使用无处不在的数据源预测流量状况。当前，对于高峰时段等经常性交通拥堵，有完善的预测模型。但是，需要预测由非经常性事件（例如高速公路封闭）引起的交通拥堵。随着各机构开始了解收集工作区数据的价值，将会出现由历史工作区信息组成的丰富数据集。在大数据时代，丰富的移动性数据源变得可用，这使得机器学习的应用能够预测工作区域的移动性。这项研究的目的是利用有监督的机器学习算法利用历史车道封闭信息来预测未来车道封闭的时空流动性。在2014年至2017年之间，从密歇根州际州的1,160个工作区收集了各种交通数据来源。本研究使用探测车辆数据来检索这些历史观测的机动性概况，并使用这些概况应用随机森林，XGBoost和人工神经网络（ ANN）分类算法。迁移率预测结果表明，人工神经网络模型的准确率高达85％，优于其他模型。这项研究的目的是证明即使没有小时流量，机器学习算法也可以用于捕获非经常性交通拥堵的模式。