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Studying population prediction under micro-spatiotemporal granularity is of great significance for modern and refined urban traffic management and emergency response to disasters. Existing population studies are mostly based on census and statistical yearbook data due to the limitation of data collecting methods. However, with the advent of techniques in this information age, new emerging data sources with fine granularity and large sample sizes have provided rich materials and unique venues for population research. This article presents a new population prediction model with micro-spatiotemporal granularity based on the long short-term memory (LSTM) and cellular automata (CA) models. We aim at designing a hybrid data-driven model with good adaptability and scalability, which can be used in more refined population prediction. We not only try to integrate these two models, aiming to fully mine the spatiotemporal characteristics, but also propose a method that fuses multi-source geographic data. We tested its functionality using the data from Chongming District, Shanghai, China. The results demonstrated that, among all scenarios, the model trained by three consecutive days (ordinary dates), with the granularity of one hour, incorporated with road networks, achieves the best performance (0.905 as the mean absolute error) and generalization capability.

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Despite the use of automation technology in the maritime industry, human errors are still the typical navigational risk factors in Maritime Autonomous Surface Ships with the third degree of autonomy, as defined by the International Maritime Organization. To analyse these human errors, a prediction model for human errors in the emergency disposal process is present. First, the risk factors are identified by analysing the emergency disposal behaviour process of a Shore Control Centre (SCC) under remote navigation mode. This is followed by the establishment of an event tree model of human errors using Technique for Human Error Rate Prediction (THERP). Furthers, a Bayesian Networks (BNs) model based on the THERP is proposed for the three stages: perception, decision, and execution. Subsequently, expert judgments based on the fuzzy theory are used to obtain the basic probability of root nodes and determine the conditional probability of each node in the BNs. Finally, the probabilities of human errors are calculated for the three stages, while the importance of human error factors is quantified with sensitivity analysis, which can provide flexible references for theoretical construction of the SCC and training of staff.