Disaster has a great impact on human mobility patterns. Clustering mobility patterns by generalizing group level characteristics from diverse individual trajectories plays a vital role in informing post-disaster recovery strategies. However, predefined clustering criteria or supervised labeling alone are insufficient to adequately capture the dynamics of mobility patterns during disaster events. Besides, the existing methods are not enough to capture the sensitive changes in mobility patterns in disaster events and handle the data of high-dimensional. This research investigates an embedded deep learning-based method which can automatically extract the groups' short-term feature of mobility patterns and achieves short-term mobility pattern clustering during disaster. The proposed method employs a Transformer-based temporal encoder to capture intra-day sequence patterns and integrates a VAE component with an embedded latent variable that directly encodes group-level mobility modes. We also design a compactness–separation loss that explicitly encourages within-mode feature compactness and between-mode feature separation. Based on massive mobile data, we conduct mobility pattern clustering on the case of 2011 Fukushima Earthquake. Compared to conventional clustering approaches, the proposed model structure is more discriminative and can capture the more sensitive changes between pre- and post- events. Compared to baselines with different loss functions, proposal methods can make more accurate fitting result and obtain more discrete clusters modes. Additionally, sensitivity analysis is conducted to examine the influence of key hyper parameters within the model. Based on the clustering outcomes, five representative mobility patterns are identified. We further analyze the spatial-temporal characteristics of mobility pattern changes during the disaster events and the recovery period of mobility pattern.

Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection. In this study, we propose a confirmed case-driven time-series prediction of crowd in urban hot spot named MobCovid. The model is a deviation of Informer, a popular time-serial prediction model proposed in 2021. The model takes both the number of nighttime staying people in downtown and confirmed cases of COVID-19 as input and predicts both the targets. In the current period of COVID, many areas and countries have relaxed the lockdown measures on public mobility. The outdoor travel of public is based on individual decision. Report of large amount of confirmed cases would restrict the public visitation of crowded downtown. But, still, government would publish some policies to try to intervene in the public mobility and control the spread of virus. For example, in Japan, there are no compulsory measures to force people to stay at home, but measures to persuade people to stay away from downtown area. Therefore, we also merge the encoding of policies on measures of mobility restriction made by government in the model to improve the precision. We use historical data of nighttime staying people in crowded downtown and confirmed cases of Tokyo and Osaka area as study case. Multiple times of comparison with other baselines including the original Informer model prove the effectiveness of our proposed method. We believe our work can make contribution to the current knowledge on forecasting the number of crowd in urban downtown during the Covid epidemic.

Activity pattern prediction is a critical part of urban computing, urban planning, intelligent transportation, and so on. Based on a dataset with more than 10 million GPS trajectory records collected by mobile sensors, this research proposed a CNN-BiLSTM-VAE-ATT-based encoder-decoder model for fine-grained individual activity sequence prediction. The model combines the long-term and short-term dependencies crosswise and also considers randomness, diversity, and uncertainty of individual activity patterns. The proposed results show higher accuracy compared to the ten baselines. The model can generate high diversity results while approximating the original activity patterns distribution. Moreover, the model also has interpretability in revealing the time dependency importance of the activity pattern prediction.

Life pattern clustering is essential for abstracting the groups' characteristics of daily life patterns and activity regularity. Based on millions of GPS records, this research proposes a framework on the life pattern clustering which can efficiently identify the groups that have similar life patterns. The proposed method can retain original features of individual life pattern data without aggregation. Metagraph-based data structure is proposed for presenting the diverse life pattern. Spatial-temporal similarity includes significant places semantics, time-sequential properties and frequency are integrated into this data structure, which captures the uncertainty of an individual and the diversities between individuals. Non-negative-factorization-based method is utilized for reducing the dimension. The results show that our proposed method can effectively identify the groups that have similar life pattern in long term and takes advantage in computation efficiency and representational capacity compared with the traditional methods. We reveal the representative life pattern groups and analyze the group characteristics of human life patterns during different periods and different regions. We believe our work helps in future infrastructure planning, services improvement and policy making related to urban and transportation, thus promoting a humanized and sustainable city.

Human mobility similarity comparison plays a critical role in modeling city dynamics, which exerts an enormous impact on developing intelligent transportation system. By expanding origin-destination matrix, we propose a mobility expression named mobility tableau and corresponding similarity measurement approach. Compared with traditional Origin-Destination matrix-based mobility comparison, mobility tableau comparison provides multi-dimensional similarity information, including volume similarity, spatial similarity, mass inclusiveness and structure similarity. The robustness of the measure is supported through several sensitive analysis based on real Global Positioning System dataset. The better performance of our proposed approach compared with traditional methods in two case studies including Call Detail Record based mobility tableau validation and different cities’ mobility comparison also demonstrates the practicality and superiority of our method.

How to effectively extract identifiable information from human mobility data and distinguish different agents is a significant topic for location-based services and intelligent transportation systems, which is described as the Human Mobility Signature Identification problem. A deeper understanding of the identifiable information underlain in human mobility can help us lay the foundation for applications such as irregular user behavior detection and privacy protection. However, human mobility comprises a mixture of different mobility patterns, traditional methods usually pay more attention to spatial-temporal features, while pattern dimension feature is usually ignored, which makes the result very dependent on the population agglomeration degree. To bridge the research gap, in this paper, we propose a novel Life-Travel pattern-based learning module (LTP-Net), in which spatial-temporal-pattern dimension features are embedded together to provide more comprehensive information for individual identification. A real-world mobile phone location dataset is utilized to evaluate the performance of the proposed LTP-Net and traditional methods. Several case studies are also conducted to analyze the model performance, including the abnormal behavior detection for the east Japan earthquake.

Aggregating crowd density in grids from big mobile datasets is a basic but critical work in urban computing and mobile computing. The error of position estimation in raw mobile data, including spatial deviation and temporal deviation, is inevitable and directly impacts the accuracy of aggregated crowd density results. In this case, a key modifiable areal unit problem is raised to understand the relationship among the crowd density accuracy, raw mobile data error, grid shape, and size, but few studies focused on it. This paper analyzes this modifiable areal unit problem of the error in crowd density estimation from big mobility data. By regarding the error as the result of a convolution operation, an optimization model based restoration method was proposed to fix the error of the estimated result, and we analyzed the restoration effect under different circumstances by several simulation experiments. A real application for grided population distribution map construction and restoration from Call Detail Record was conducted to prove the reliability of the whole analysis, which demonstrates the restoration method can reduce the error by nearly 40% under certain conditions.