Traffic flow imputation provides a more-complete view of traffic flows, and thus is a fundamental function in building Intelligent Transportation Systems. The performance of traffic flow imputation has a big impact on a wide range of downstream applications, such as traffic forecasting and control. Therefore, in this paper, we propose a Multi-grAph Convolutional Recurrent netwOrk (MACRO) framework for supporting fine-grained lane-level traffic flow imputation, which can help to reconstruct more complete traffic flows at the lane level. Specifically, we first design a spatial dependency module to model the diversified spatial correlations within traffic flows, where multi-relation graphs are first constructed to consider correlations from various perspective, then a multi-graph convolution neural network is proposed to capture the integrated spatial dependencies of traffic flows and adequately propagate the observed traffic values to mitigate data sparsity problem from spatial domain. Also, to handle the temporally continuous data missing issue, we adopt a modified bi-directional recurrent neural network to capture traffic flows’ temporal dependencies by considering both historical and future information, and employ a temporal decay mechanism to control the irregular information transfer between adjacent time slices. Moreover, a spatio-temporal knowledge integration module is devised to comprehensively integrate multi-resolution spatiotemporal knowledge for traffic flow imputation. Finally, extensive experiments on the real-world dataset demonstrate that the performance of MACRO outperforms several state-of-the-art baselines with respect to traffic flow imputation.

The mobile in-App service analysis, aiming at classifying mobile internet traffic into different types of service usages, has become a challenging and emergent task for mobile service providers due to the increasing adoption of secure protocols for in-App services. While some efforts have been made for the classification of mobile internet traffic, existing methods rely on complex feature construction and large storage cache, which lead to low processing speed, and thus not practical for online real-time scenarios. To this end, we develop an iterative analyzer for classifying encrypted mobile traffic in a real-time way. Specifically, we first select an optimal set of most discriminative features from raw features extracted from traffic packet sequences by a novel Maximizing Inner activity similarity and Minimizing Different activity similarity (MIMD) measurement. To develop the online analyzer, we first represent a traffic flow with a series of time windows, which are described by the optimal feature vector and are updated iteratively at the packet level. Instead of extracting feature elements from a series of raw traffic packets, our feature elements are updated when a new traffic packet is observed and the storage of raw traffic packets is not required. The time windows generated from the same service usage activity are grouped by our proposed method, namely, recursive time continuity constrained KMeans clustering (rCKC). The feature vectors of cluster centers are then fed into a random forest classifier to identify corresponding service usages. Finally, we provide extensive experiments on real-world Internet traffic data from Wechat, Whatsapp, and Facebook to demonstrate the effectiveness and efficiency of our approach. The results show that the proposed analyzer provides high accuracy in real-world scenarios, and has low storage cache requirement as well as fast processing speed.

Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. Many providers of bike sharing systems are ready to expand their bike stations from the existing service area to surrounding regions. A key to success for a bike sharing systems expansion is the bike demand prediction for expansion areas. There are two major challenges in this demand prediction problem: First. the bike transition records are not available for the expansion area and second. station level bike demand have big variances across the urban city. Previous research efforts mainly focus on discovering global features, assuming the station bike demands react equally to the global features, which brings large prediction error when the urban area is large and highly diversified. To address these challenges, in this paper, we develop a hierarchical station bike demand predictor which analyzes bike demands from functional zone level to station level. Specifically, we first divide the studied bike stations into functional zones by a novel Bi-clustering algorithm which is designed to cluster bike stations with similar POI characteristics and close geographical distances together. Then, the hourly bike check-ins and check-outs of functional zones are predicted by integrating three influential factors: distance preference, zone-to-zone preference, and zone characteristics. The station demand is estimated by studying the demand distributions among the stations within the same functional zone. Finally, the extensive experimental results on the NYC Citi Bike system with two expansion stages show the advantages of our approach on station demand and balance prediction for bike sharing system expansions.

The service usage analysis, aiming at identifying customers' messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced frame-work for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage sub-sequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach.