Traffic problems have seriously affected people's life quality and urban development, and forecasting short-term traffic congestion is of great importance to both individuals and governments. However, understanding and modeling the traffic conditions can be extremely difficult, and our observations from real traffic data reveal that: 1) similar traffic congestion patterns exist in the neighboring time slots and on consecutive workdays and 2) the levels of traffic congestion have clear multiscale properties. To capture these characteristics, we propose a novel method named PCNN, which is based on a deep convolutional neural network, modeling periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. It first temporally folds the time series and constructs a 2-D matrix as the network input, such that both the real-time traffic conditions and past traffic patterns are well considered; then, with a series of convolutions over the input matrix, it is able to model the local temporal dependency and multiscale traffic patterns. In particular, the global trend of congestion can be addressed at the macroscale, whereas more details and variations of the congestion can be captured at the microscale. Experimental results on a real-world urban traffic data set confirm that folding time series data into a 2-D matrix is effective and PCNN outperforms the baselines significantly for the task of short-term congestion prediction.

Video anomaly detection is a challenging task owing to the rare and diverse nature of abnormal events. However, most of the existing methods only learn the normality in a single space, focusing on low-level detailed features, which is easily affected by unimportant pixels. To address this issue, in this study, we propose a semi-supervised method based on the generative adversarial network and frame prediction, wherein the normality is learned in both the original image space and latent space, and the events deviating from the normality are detected as anomalies. In particular, given a video clip, we first predict a future frame and minimize the prediction errors between the generated frame and its ground truth. Thereafter, we encode the predicted frames and their ground truths in the latent space and minimize their differences. In the testing phase, we calculate the normal scores of each frame in both the image and latent spaces to obtain a comprehensive evaluation. Utilizing the multispace can capture more normality distribution information of the data, which can benefit anomaly detection. The results of experiments on three benchmark datasets demonstrate the effectiveness of the proposed method.

We present a novel approach for estimating the proportional distributions of function types (i.e. functional distributions) in an urban area through learning semantics preserved embeddings of points-of-interest (POIs). Specifically, we represent POIs as low-dimensional vectors to capture (1) the spatial co-occurrence patterns of POIs and (2) the semantics conveyed by the POI hierarchical categories (i.e. categorical semantics). The proposed approach utilizes spatially explicit random walks in a POI network to learn spatial co-occurrence patterns, and a manifold learning algorithm to capture categorical semantics. The learned POI vector embeddings are then aggregated to generate regional embeddings with long short-term memory (LSTM) and attention mechanisms, to take account of the different levels of importance among the POIs in a region. Finally, a multilayer perceptron (MLP) maps regional embeddings to functional distributions. A case study in Xiamen Island, China implements and evaluates the proposed approach. The results indicate that our approach outperforms several competitive baseline models in all evaluation measures, and yields a relatively high consistency between the estimation and ground truth. In addition, a comprehensive error analysis unveils several intrinsic limitations of POI data for this task, e.g. ambiguous linkage between POIs and functions.

As a critical task of the urban traffic services, fine-grained urban flow inference (FUFI) benefits in many fields including intelligent transportation management, urban planning, public safety. FUFI is a technique that focuses on inferring fine-grained urban flows depending solely on observed coarse-grained data. However, existing methods always require massive learnable parameters and the complex network structures. To reduce these defects, we formulate a contrastive self-supervision method to predict fine-grained urban flows taking into account all correlated spatial and temporal contrastive patterns. Through several well-designed self-supervised tasks, uncomplicated networks have a strong ability to capture high-level representations from flow data. Then, a fine-tuning network combining with three pre-training encoder networks is proposed. We conduct experiments to evaluate our model and compare with other state-of-the-art methods by using two real-world datasets. All the empirical results not only show the superiority of our model against other comparative models, but also demonstrate its effectiveness in the resource-limited environment.

Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose the maximum predictability to derive the theoretical limits of accuracy that any predictive algorithm could achieve on predicting urban human mobility. However, existing maximum predictability only considers the sequential patterns of human movements and neglects the contextual information such as the time or the types of places that people visit, which plays an important role in predicting one's next location. In this paper, we propose new theoretical limits of predictability, namely Context-Transition Predictability, which not only captures the sequential patterns of human mobility, but also considers the contextual information of human behavior. We compare our Context-Transition Predictability with other kinds of predictability and find that it is larger than these existing ones. We also show that our proposed Context-Transition Predictability provides us a better guidance on which predictive algorithm to be used for forecasting the next location when considering the contextual information. Source code is at https://github.com/zcfinal/ContextTransitionPredictability.