Discovering Causes of Traffic Congestion via Deep Transfer Clustering

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim at discovering the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: (1) traffic congestion is affected by several factors with complex spatio-temporal relations; (2) there are a few samples of congestion data with known causes due to the limitation of human label; (3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: (1) congestion feature extraction module, which extracts the important features distinguishing between different causes of congestion; and (2) congestion cause discovery module, which designs a deep semi-supervised learning based framework to discover the causes of traffic congestion with limited labeled data. Specifically, in pre-training stage, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, in clustering stage, we propose two different clustering methods to discover the congestion causes. For the first clustering method, we extend the classic deep embedded clustering model to produce clusters via soft assignment. For the second one, we iteratively use k-means to group the latent features extracted from the pre-trained model, and use the cluster results as pseudo-labels to fine-tune the network. Extensive experiments show that the performance of our methods is superior to the state-of-the-art baselines, which demonstrates the effectiveness of the proposed cause discovery system. Additionally, our system is deployed and used in the practical production environment at Amap.