With the rapid development of information technology at the beginning of the 21st century, the traditional transportation system is rapidly transforming to Intelligent Transportation System (ITS). Meanwhile, as a powerful optimization tool for the applications’ performance under the framework of ITS, the traffic prediction model has gained much attention. The traffic prediction model belongs to the traditional time series prediction model, widely used in industry for business analysis, abnormalities detection, etc. There are two main categories of traffic prediction models — the parametric model and the non-parametric model. The non-parametric model develops rapidly in recent years due to the machine learning theory’s maturing and increment in computing power. Compared to the parametric model, the non-parametric model is more accurate and requires less advanced analysis of traffic patterns’ correlation, and can better handle a large amount of historical data. However, the non-parametric model also requires more powerful devices for training and implementation. The traffic prediction system’s implementation environment, built by on-road resources and off-road infrastructures, is relatively limited compared to the traditional data center. This paper uses several well-known state-of-the-art non-parametric models and their Deep Learning structures for traffic prediction, and evaluates the models’ performance on both freeway and urban-road dataset. Focusing on the model’s accuracy and training cost while deploying prediction models in a large-scale traffic network, this paper provides a novel optimized training strategy called CTS to reduce the implementation cost of a complex inner structure model. This approach could be further used to reduce the deployment cost, especially the training time, for the big data intelligent system using machine learning.

随着21世纪初信息技术的飞速发展，传统的交通系统正在迅速向智能交通系统（ITS）转变。同时，作为一种在ITS框架下针对应用程序性能的强大优化工具，流量预测模型已引起了广泛关注。流量预测模型属于传统的时间序列预测模型，在行业中广泛用于业务分析，异常检测等。流量预测模型主要有两类：参数模型和非参数模型。由于机器学习理论的成熟和计算能力的提高，非参数模型近年来发展迅速。与参数模型相比，非参数模型更准确，并且不需要对交通模式的相关性进行更高级的分析，并且可以更好地处理大量历史数据。但是，非参数模型还需要功能更强大的设备来进行培训和实施。与传统数据中心相比，由公路资源和越野基础设施构建的交通预测系统的实施环境相对有限。本文使用了几种著名的最新非参数模型及其深度学习结构来进行交通预测，并评估了模型在高速公路和城市道路数据集上的性能。在大规模交通网络中部署预测模型的同时，着重于模型的准确性和培训成本，本文提供了一种称为CTS的新型优化培训策略，以减少复杂的内部结构模型的实现成本。对于使用机器学习的大数据智能系统，该方法可进一步用于减少部署成本，尤其是培训时间。