Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Data missing <s> This paper proposes a method to recover missing data during observation by factorial hidden Markov models (FHMMs). The fundamental idea of the proposed method originates from the mimesis model, inspired by the mirror neuron system. By combining the motion recognition from partial observation algorithm and the proto-symbol based duplication of observed motion algorithm, whole body motion imitation from partial observation can be achieved. The algorithm for missing data recovery uses the same basic strategy as the whole body motion imitation from partial observation, but requires more accurate spatial representability. FHMMs allow for more efficient representation of a continuous data sequence by distributed state representation compared to hidden Markov models (HMMs). The proposed algorithm is tested with human motion data and the experimental results show improved representability compared to the conventional HMMs. <s> BIB001 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Data missing <s> Predicting the movement of crowds in a city is strategically important for traffic management, risk assessment, and public safety. In this paper, we propose predicting two types of flows of crowds in every region of a city based on big data, including human mobility data, weather conditions, and road network data. To develop a practical solution for citywide traffic prediction, we first partition the map of a city into regions using both its road network and historical records of human mobility. Our problem is different than the predictions of each individual's movements and each road segment's traffic conditions, which are computationally costly and not necessary from the perspective of public safety on a citywide scale. To model the multiple complex factors affecting crowd flows, we decompose flows into three components: seasonal (periodic patterns), trend (changes in periodic patterns), and residual flows (instantaneous changes). The seasonal and trend models are built as intrinsic Gaussian Markov random fields which can cope with noisy and missing data, whereas a residual model exploits the spatio-temporal dependence among different flows and regions, as well as the effect of weather. Experiment results on three real-world datasets show that our method is scalable and outperforms all baselines significantly in terms of accuracy. <s> BIB002 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Data missing <s> Many sensors have been deployed in the physical world, generating massive geo-tagged time series data. In reality, readings of sensors are usually lost at various unexpected moments because of sensor or communication errors. Those missing readings do not only affect real-time monitoring but also compromise the performance of further data analysis. In this paper, we propose a spatio-temporal multi-view-based learning (ST-MVL) method to collectively fill missing readings in a collection of geosensory time series data, considering 1) the temporal correlation between readings at different timestamps in the same series and 2) the spatial correlation between different time series. Our method combines empirical statistic models, consisting of Inverse Distance Weighting and Simple Exponential Smoothing, with data-driven algorithms, comprised of User-based and Item-based Collaborative Filtering. The former models handle general missing cases based on empirical assumptions derived from history data over a long period, standing for two global views from spatial and temporal perspectives respectively. The latter algorithms deal with special cases where empirical assumptions may not hold, based on recent contexts of data, denoting two local views from spatial and temporal perspectives respectively. The predictions of the four views are aggregated to a final value in a multi-view learning algorithm. We evaluate our method based on Beijing air quality and meteorological data, finding advantages to our model compared with ten baseline approaches. <s> BIB003
Due to sensor failures, communication errors, and other human factors, spatial-temporal data is often missing. Data missing often brings negative impact to the subsequent data analysis, so it is necessary to study the problem of data missing. At present, the main data missing processing method is to fill the missing value. For example, Lee et al. BIB001 proposed a factorial hidden Markov model to revover missing values. Hoang et al. BIB002 divided a city into low-level regions based on road network, and grouped adjacent these regions with similar crowd flow patterns using graph clustering. It's a novel and effective solution, but it's not sure how to define similar crowd flow patterns accurately. To consider temporal and spatial correlation, Yi et al. BIB003 proposed a spatial-temporal multi-view-based learning (ST-MVL) methods to collectively fill missing value in a collection of geo-sensory time series data. This method received good performance because of combining empirical statistic models, consisting of Inverse Distance Weighting and Simple Exponential Smoothing, with User-based and Item-based Collaborative Filtering. Hence, we can conclude that data missing in spatial-temporal datasets have an implicit spatiotemporal correlation.