Route Restoration Method for Sparse Taxi GPS trajectory based on Bayesian Network

In order to improve the availability of taxi GPS big data, we restore the chosen route for the sparse taxi GPS trajectory in this work. A trajectory restoration method based on Bayesian network is proposed. Compared with the traditional research solely based on time-spatial variables, this method additionally considers the characteristics of empty/heavy taxi status, weather conditions, drivers, vehicle running and other factors to carry out route restoration. A field case of grid network in Ningbo is taken to verify the applicability of the method, using the taxi GPS trajectory data from Ningbo Taxi Information Management Platform. The case results show that the accuracy of Bayesian network method based on multiple factors reaches 91.4%. Its performance is superior to the Multivariate logistic regression model. In addition, the proposed method is especially suitable for scenarios with a high missing rate of track data, such as a scene with timespan of about 5 min between neighbour trajectories

Causal Consistent Databases

Many consistency criteria have been considered in databases and the causal consistency is one of them. The causal consistency model has gained much attention in recent years because it provides ordering of relative operations. The causal consistency requires that all writes, which are potentially causally related, must be seen in the same order by all processes. The causal consistency is a weaker criteria than the sequential consistency, because there exists an execution, which is causally consistent but not sequentially consistent, however all executions satisfying the sequential consistency are also causally consistent. Furthermore, the causal consistency supports non-blocking operations; i.e. processes may complete read or write operations without waiting for global computation. Therefore, the causal consistency overcomes the primary limit of stronger criteria: communication latency. Additionally, several application semantics are precisely captured by the causal consistency, e.g. collaborative tools. In this paper, we review the state-of-the-art of causal consistent databases, discuss the features, functionalities and applications of the causal consistency model, and systematically compare it with other consistency models. We also discuss the implementation of causal consistency databases and identify limitations of the causal consistency model

Efficient traffic congestion estimation using multiple spatio-temporal properties

Traffic estimation is an important issue to analyze the traffic congestion in large-scale urban traffic situations. Recently, many researchers have used GPS data to estimate traffic congestion. However, how to fuse the multiple data reasonably and guarantee the accuracy and efficiency of these methods are still challenging problems. In this paper, we propose a novel method Multiple Data Estimation (MDE) to estimate the congestion status in urban environment with GPS trajectory data efficiently, where we estimate the congestion status of the area through utilizing multiple properties, including density, velocity, inflow and previous status. Among them, traffic inflow and previous status (combination of time and space factors) are not both used in other existing methods. In order to ensure the accuracy and efficiency, we apply dynamic weights of data and parameters in MDE method. To evaluate our methods, we apply it on large-scale taxi GPS data of Beijing and Shanghai. Extensive experiments on these two real-world datasets demonstrate the significant improvements of our method over several state-of-the-art methods