Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India.
Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.
PLoS One. 2022 Sep 23;17(9):e0275030. doi: 10.1371/journal.pone.0275030. eCollection 2022.
Modeling and prediction of traffic systems is a challenging task due to the complex interactions within the system. Identification of significant regressors and using them to improve travel time predictions is a concept of interest. In previous studies, such regressors were identified offline and were static in nature. In this study, an iterative joint clustering and prediction approach is proposed to accurately predict spatiotemporal patterns in travel time. The clustering module is tied to the prediction module, and a prediction model is trained on each cluster. The combined clustering and prediction are then iterated until a chosen metric is optimized. This orients clusters of data towards prediction while enabling model development on subsets of travel time data with similar prediction complexity. The clusters created using the joint clustering and prediction approach confirmed to the real-world traffic scenario, forming clusters of high travel time at busy intersections and bus stops across the study stretch and forming clusters of low travel time in the sub-urban areas of the city. Further, a comparison of the developed framework with base methods demonstrated a decrease in prediction errors by at least 22.83%. This indicates that creating clusters of data that are sensitive to the quality of predictions using the joint clustering and prediction framework improves the accuracy of travel time predictions. The study also proposes criteria for choosing the best predictions when cluster-based predictions are used.
交通系统建模和预测是一项具有挑战性的任务，因为系统内部存在复杂的相互作用。识别重要的回归变量并利用它们来提高行程时间预测的准确性是一个备受关注的概念。在之前的研究中，这些回归变量是离线识别的，并且具有静态性质。在本研究中，提出了一种迭代的联合聚类和预测方法，以准确预测行程时间的时空模式。聚类模块与预测模块相关联，并在每个聚类上训练预测模型。然后，对联合聚类和预测进行迭代，直到选择的指标得到优化。这种方法使聚类朝着预测的方向发展，同时能够在具有相似预测复杂度的行程时间数据子集上开发模型。使用联合聚类和预测方法创建的聚类证实了现实交通场景的情况，在研究路段的繁忙路口和公共汽车站形成了高行程时间的聚类，在城市的郊区形成了低行程时间的聚类。此外，将所开发的框架与基准方法进行比较表明，预测误差至少降低了 22.83%。这表明，使用联合聚类和预测框架创建对预测质量敏感的数据聚类可以提高行程时间预测的准确性。本研究还提出了在使用基于聚类的预测时选择最佳预测的标准。
