RELATED WORKS
Traditional Deep Learning Models in Mobility Prediction Since mobility prediction tasks have been widely studiedin recent years,,, there are many mobility prediction models using deep learning methods. Feng et. al. proposed DeepMove to use RNN and attention to extract time and location embeddings fortrajectory prediction. Flashback enhances the prediction performance of RNNs by emphasizing periodic and contextual information. STiSAN+
further refines predictions by considering both daily and hourly timing along with spatial relationships. SSDL represents the complex human mobility semantics in different hidden spaces by decoupling the time-invariant and timevarying factors, and the POI graph structure is designed to explore the heterogeneous cooperation signals in the historical mobility. STAR captures spatio-temporal correspondence by designing different spatio-temporal maps and building a stay branch to simulate the stay time in different locations, which is ultimately optimized through adversarial training. There are also works using transfer learning to extract knowledge from other cities to enhance the prediction in the current city.
CHAML considers both city-level and userlevel difficulty to enhance conditional sampling in the metatraining process. Xu et. al. propose CrossPred that takes into account multiple features for cross-city POI matching and uses maximum mean difference (MMD) to enhance shared POI features between cities. However, these models do not considerthe mobility pattern shift in disaster scenarios. Thus, a series of mobility prediction models specially designed for disaster scenarios are proposed. Song et. al. proposed a system using HMM to model human behavior and use MDP to generate human mobility in disaster scenarios. DeepMob uses multi-modal learning to combine the information of behavior and mobility by a shared representation in the deep neural network and achieves a performance gain in both behavior and mobility prediction. WS-BiGNN represents mobile and network search records as link prediction tasks by constructing a bipartite graph, andintroduces a time-weighted module and a searchmobile memory module to achieve the prediction of individual irregular mobility. LLM-enhanced Models in Mobility Prediction Recently, as LLMs have proved to have great advantages in understanding human behavior, more and more researchers have begun to explore LLMs for mobility prediction. Wang et. al. introduced LLMMob to capture both long-term and short-term mobility patterns by considering ”historical” and ”contextual” stays, andincorporating time information for more accurate predictions. LLM4POI can understand the intrinsic meaning of context information while maintaining the original format of heterogeneous location data, thus avoiding information loss and realizing POI recommendation. AgentMove enhances mobility prediction by mining individual patterns, modeling urban effects, and extracting shared population behaviors. However, these models are designed for mobility prediction in normal scenarios and are unable to handle the mobility shift in disaster scenarios.
III. PROBLEM DEFINITION
DEFINITION 1 (Location): A location r is defined as an area of a specific geographical space such as an administration, a street block, or a community. It is associated with attributes including POI category, POI number, etc. Different locations may also have interactions and relationships based on the road network and transportation facility of the city. Based on the definition above, the human mobility trajectory can be defined as follows. Soft Prompt
DEFINITION 2 (Human Mobility Trajectory): A human mobility trajectory is formulated as a location sequence S 0: t = {r 0 , r 1 ...rt } in which rt indicates the location where the individual is located at timestamp t . Based on the mobility prediction task,,,,
, we define the task of human mobility prediction in disaster scenarios as follows. PROBLEM 1 (Human Mobility Prediction in Disaster Sce nario): For a given city that experiencing a disaster, we have a user’s mobility trajectory S 0: [D] T − 1 [and a set of historical] trajectories of this city in normal scenarios {S} [N], the goal is to predict the next location r T of the user. This prediction can be enhanced by incorporating external information such as the disaster level d and mobility trajectories {S} [ref] from other cities in normal and disaster scenarios. IV. METHOD
A. Overall Framework
To predict human mobility in disaster scenarios, we propose an LLM-driven transfer learning framework named DisasterMobLLM. This framework enhances existing deep mobility prediction models with intention modeled by LLMs and transfers knowledge of how disaster affects mobility patterns among cities and scenarios. The deep mobility prediction model usedin the framework is referred to as the base model. In addition, the city experiencing the disaster is denoted as the target city, and other cities are denoted as source cities. The overall structure of our framework is shown in Fig. 2, which is composed of three modules with the information flow between them shown in Fig. 3. Specifically, we initially introduce an RAG-Enhanced Intention Predictorto extract historical intention sequences from usertrajectories with unsupervised learning and predict the next intention. In this part, we train an intention translator and predictor E I on the data of normal scenarios in both source and target cities as shown in the green rectangle of Intention Prediction in Fig. 3. Then we introduce the LLM-based Intention Refinerto refine the predictedintention. In this module, we fine-tune an LLM to understand how different disasters affect human mobility patterns in the data of disaster scenarios of source cities as shown in the orange rectangle of Intention Refinement in Fig. 3. Finally, we use an Intention-Modulated Location Predictor with a deep-learning mobility prediction model as the base model to predict the specific location of the next timestamp with intention modulation. In this part, we train the base model in normal scenarios of the target city to learn how intention affects the prediction of the next location in the target city as shown in the red rectangle of Location Prediction in Fig. 3. Thus, the framework can transferthe knowledge of how different disaster affects human mobility patterns in different cities and predict the next location in disaster scenarios. B. RAG-Enhanced Intention Predictor
In this module, we train an intention translator and predictor E I to predict the next intention of the given trajectory. Furthermore, we also retrieve a set of reference trajectories to address the data sparsity problem of trajectories in the target city forthe target disaster.
1) Intention Translator and Predictor: As mentionedin Sec. I, the mobility patterns shift in the disaster scenario, and it is difficult to transfer between cities. Thus, we convert the trajectory S 0: [D] T − 1 [to the intention level and predict the next] intention X T [D] [using an intention translator and predictor] [ E] [I] [.]
each type of road between two locations as the travel feature. Then we map the feature of each travel in the travel feature sequences to a sharedintention space to obtain the intention feature sequences in intention modality X 1: [D] T − 1 [for each tra-] jectory. This process, defined as intention mapping, includes a Transfer Component Analysis (TCA) on all travel feature sequences in all source cities and an unsupervised clustering. We consider each cluster as an intention following and use the feature of the central sample of each cluster as the target intention feature, thus obtaining an intention feature sequence for each trajectory. Aside from that, in the intention feature sequences, we separately assign an immobility embedding to all travels in which the location is not changed. We define the data form obtained from the above method as intention
modality. However, the LLMs cannot understand these intention features because they are pre-trainedin the language modality while the intention feature sequence is in the intention modality. Thus, we use a transformerthat receives a travel feature sequence F 1: [D] T − 1 [and outputs the embedding of the] next intention YT [D] at language modality to align the intention feature sequence to the language modality. This process can be formulated as follows:
YT [D] [=] [ E] [I] � F 1: [D] T − 1 � ,
in which F 1: [D] T − 1 [refers to any travel feature sequences includ-] ing the given trajectories and reference trajectories.
It should be noticed that the output is the next intention YT [D] because we use the time series modeling capability of the transformerto predict the next intention. 2) Intention-CLIP: To train the intention translator and predictor E I, we propose C ontrastive L anguage- Intention P retraining ( Intention-CLIP ) illustratedin Fig. 4. It achieves the prediction of the next intention and the alignment of the language-intention modality at the same time. Inspired by–, we design an attention mechanism based on LLM’s vocabulary table to achieve alignment. We firstly pass the vocabulary table of the LLM V ∈R [N] [V] [ ×][D] [V] through a weight allocation function h to obtain the matrix of intentionrelated prototypes P ∈R [N] [P] [ ×][D] [V] indicating a compressedintention feature. The weight allocation function h represents the weights of all combinations of the intention-related vocabularies to the prototypes. This process can be formulated as follows:
P = h ( V ) ,
in which NV refers to the number of LLM vocabulary, D V refers to the dimension of LLM vocabulary embeddings and
When conducting Intention-CLIP, we obtain the trajectory embedding by feeding the first T − 1 elements in the location intention-oriented sequence into the intention translator and predictor E I to obtain the prediction of the next intention Y t . We then update the weights of the intention translator and predictor, the attention mechanism, and the weight allocation function by optimizing the InfoNCE loss and crossentropy loss as follows:
LC = InfoNCE ( T t , Y t ) + CE ( T t , Y t ) ,
in which InfoNCE refers to the InfoNCE loss and CE refers to the Cross-entropy loss. In addition, for all immobility travels, we initialize their embedding as the embedding after passing “stay still” to the tokenizer of the LLM and optimize this embedding together using LC . Thus, as E I is trainedin D [N] S [and][ D] [N] T transfer patterns in normal scenarios and output the feature of the next intention in the language modality YT given an intention-oriented feature sequence X 0: T − 1 . 3) Trajectory Retrieval: To address the sparsity of trajectories in the target city of the target disaster, we retrieve reference trajectories set {S [ref] } that are most similarto S 0: [D] T − 1 [from the database as external knowledge forthe] prediction. It includes trajectories that are most similarto S 0: [D] T − 1 [at the intention level among all trajectories with disas-] ter level din the source cities {S [D,][C] [S] } [ref], and trajectories that are most similarto the intention feature sequence of S 0: [D] T − 1 [among all trajectories in the target city in normal] scenarios or other disaster scenarios {S [D] [′] [,][C] [T] } [ref] . We use the travel feature extraction andintention mapping to obtain their intention feature sequences X 1: [D] T − 1 [and] [ X] [′] [ and use Dynamic]
NP refers to the number of prototypes, which is a hyperparameter. Then, we use an attention mechanism to obtain the relationship between the intention feature and prototypes by using X t as query and Pas key and value to obtain the intention embedding at the language modality T t .
T t = softmax X t PT � ~~√~~ d k
P. � TABLE I: Performance in disaster scenarios in target city. The best results are in bold and the suboptimal results are underlined. Model Acc@1 Acc@10 MRR NDCG@5 NDCG@10 Pre@Immob Rec@Immob F1@Immob
LSTM 0.0381 0.1892 0.0436 0.0729 0.1109 0.1698 0.1375 0.1518
GRU 0.0367 0.1649 0.0359 0.0682 0.0903 0.1624 0.1334 0.1465
DeepMove@LSTM 0.1354 0.2796 0.1832 0.1917 0.3198 0.3358 0.3297 0.3325 Flashback@LSTM 0.0854 0.2143 0.1167 0.1633 0.2874 0.2781 0.2219 0.2468
STiSAN+ 0.1872 0.3198 0.2187 0.2465 0.3650 0.3681 0.3819 0.3749
HMM+MDP 0.1532 0.3127 0.1970 0.2166 0.3319 0.3613 0.3597 0.3605
DeepMob 0.2180 0.4290 0.2501 0.2789 0.3701 0.3871 0.3540 0.3699 CHAML 0.1377 0.2097 0.1224 0.1401 0.2998 0.2412 0.2588 0.2496
CATUS 0.1781 0.2297 0.1402 0.1621 0.3176 0.2776 0.2809 0.2792
LLM4POI 0.1186 0.2413 0.1522 0.1612 0.2976 0.2971 0.3087 0.3031
ST-MoE-BERT 0.2119 0.4187 0.2478 0.3198 0.3699 0.3387 0.3290 0.3337
Ours@Flashback 0.1579 0.3342 0.1398 0.2022 0.3376 0.4207 0.4398 0.4301
Ours@DeepMove 0.2587 0.4982 0.2905 0.3097 0.3578 0.4863 0.4901 0.4882 Ours@STiSAN+ 0.2897 0.5118 0.3209 0.3283 0.4601 0.5012 0.5114 0.5062
Time Wrapping to measure the similarity between two trajectories. The process can be formulated as:
{S} [ref] = {S [D,][C] [S] } [ref] ∪{S [D] [′] [,][C] [T] } [ref] . A more detailed description of the trajectory retrieval process can be foundin Appendix VII-A.
C. LLM-based Intention Refiner
Although we have initially predicted the feature of the next intention Y [ˆ] T [D] [in the RAG-Enhanced Intention Predictor, it only] captures the common patterns of human mobility in normal scenarios because E I is trained on the dataset for normal scenarios in source cities. Since LLM is proven to be powerful in understanding human behaviors, we use it to refine the predicted next intention feature YT [D] based on the reference intention feature sequences {Y } [ref] and the target disasterˆ level d to get the refinedintention feature X T [D] [. Specifically, we] introduce a hybrid prompting strategy including an intentionincorporated prompt with immobility modeling and a disasterlevel-awaring soft prompt to stimulate the understanding of human behaviors of LLM in refining the intention. 1) Intention-Incorporated Prompt: Different from the traditional prompting strategy of LLMs which inputs the tokenized embedding of the text instruction, we design an intentionincorporated prompt inspired by– to better inspire the LLMs to understandintention information. In this prompt, we encode the text instructions into token embeddings using the tokenizer of the LLM while directly using the intention feature sequence as the embeddings for intention tokens to construct the intention-incorporated embeddings Z [h] . Furthermore, to stimulate the LLM’s ability to understand human behavior and to better guide LLMs to understand immobility in the disaster scenario at the intention level, we introduced a Chain-of-Thought prompt framework to refine the intention. The thinking process contains three steps. Firstly, we guide the LLM to decide whetherthe predicted next intention result is right. If not, we guide LLM to distinguish whetherthe next intention is immobility. If not, we guide LLMs to decide which intention should be selected as the next intention given all the non-immobility intention embeddings. See Appendix VIII-D for more details. 2) Disaster-Level-Awaring Soft Prompt: In orderto address the heterogeneity of mobility patterns between different disasters, we use a disaster encoder ED to construct an adaptive embedding Z [d] given the disaster level d to model the disaster level. The disaster encoder is instantiated as a look-up table and the encoding process is formulated as:
Z [d] = ED ( d ) ,
in which Z [d] refers to the disaster embedding. We use the disaster embedding Z [d] in a soft prompt paradigm to model the heterogeneity of different disasters. Different from the traditional hard prompt, a soft prompt adjusts the behaviors of the LLM by learning a set of trainable embeddings to be used as prefixes forthe input sequences.
This approach can significantly reduce the number of parameters that need to be updated while maintaining or improving the performance of the model. Thus, the construction process of the input embedding sequence I can be formulated as follows:
I = Concat � Z [d] , Z [h] [�] . When fine-tuning the LLM to learn how different disaster affects human mobility patterns, we use prefix-tuning to fine-tune the LLM without changing most of the parameters. We use Eq. 7 to build the input embedding sequence I andinput it into the body of LLM without the tokenizerto obtain the predicted next intention label. Then, we use it to index the corresponding intention feature in intention modality to obtain the feature forthe next intention X [ˆ] TD forthe given trajectory in the target city. Then we use cross-entropy loss to optimize the model. The loss function can be formulated as follows:
LP = CE ( X [ˆ] T [D] [, X] T [D] [)] [.]
D. Intention-Modulated Location Predictior
ˆ After obtaining the refinedintention feature X T [D] [, we use] the Intention-Modulated Location Predictorto predict the specific location where the individual will go in the next timestamp ˆ r T with intention modulation.
EXPERIMENTS
A. Datasets
Through collaboration with a popular mobile application vendor, we collect 7 real-world human trajectory datasets in 7 cities during periods of natural disasters, whose statistics are presentedin Appendix VIII-A. In particular, each dataset contains historical requested positioning data from mobile users, exhibiting spatial and temporal characteristics of human mobility in disaster scenarios. We split them by setting Zhongshan as the target city and the other cities as source cities.
B. Baselines and Experimental Settings
Baselines Different categories of baselines are compared with DisasterMobLLM. Specifically, we select traditional mobility prediction models including LSTM, GRU, DeepMove, Flashback, and STiSAN+. We also select HMM+MDP and DeepMob which are specially designed for disaster scenarios. Furthermore, we select models using cross-city knowledge to enhance the prediction performance including CHAML and CATUS. Except fortraditional deep mobility prediction models, we also use mobility prediction models enhanced by LLM, such as LLM4POI and ST-MoE-BERT. A more detailed description of baselines can be foundin Appendix VIII-B. Metrics To compare the performance of DisasterMobLLM and all types of baseline methods, we choose average Accuracy@N, Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (NDCG) as general metrics in evaluations. Moreover, to further delve into the performance enhancement for immobility, we use Precision (Pre@Immob), Recall (Rec@Immob), and F1-score for experimental studies with labeled immobility timestamps. A more detailedintroduction of the metrics can be foundin Appendix VIII-C. Experimental Settings To implement a fair comparison between different algorithms, we train LSTM, GRU, DeepMove, Flashback, and STiSAN+ on D [N] T [, train HMM+MDP and] DeepMove on one part of D [D] T [, and train CHAML and CATUS] on D [D] San instruction with the disaster level and train them on D [N] T [.] For DisasterMobLLM, we apply it to different base models to conduct comprehensive evaluations, in which we use Llama3-8B as the LLM of our framework. Finally, for all models, we conduct testing in the other parts of D [D] T [.]
C. Overall Performance
The performance comparison of all models is shown in Table I. In comparison with the best-performing baseline methods, DisasterMobLLM with STiSAN+ as the base model shows remarkable enhancements, with a 32.8% improvement in Acc@1, a 19.3% improvement in Acc@10, a 28.3% improvement in MRR, a 2.6% improvement in terms of NDCG@5, a 24.3% improvement in NDCG@10, a 29.4% improvement in Pre@Immob, a 33.9% improvement in Rec@Immob, and a 35.0% improvement in F1-score@Immob. The key reason is that our method takes the impact of disasters on human mobility patterns into account and we explicitly model the immobility in disaster scenarios. We stimulate the power of LLM in understanding how disaster affects human mobility patterns by using a hybrid tokenize strategy. Moreover, DisasterMobLLM transfers cross-city knowledge at the intention level instead of directly transferring the entire mobility trajectory, thus achieving more accurate prediction results.
D. Impact of Base Models in Different Sceanrios
In this part, we evaluate the capability of DisasterMobLLM to capture shifts in human mobility during disaster scenarios and compensate for performance degradation across different base models. First, we directly use base models to make predictions on trajectories in normal scenarios. Second, we apply base models for human mobility prediction in disaster scenarios. Third, we integrate base models with intention modulation to make predictions in disaster scenarios. The results shown in Table II illustrate that the performance of different baseline methods decreases significantly in disaster scenarios, for up to 82.2% in terms of Acc@1, 65.2% in Acc@10, and 61.4% in MRR. Once combined with the proposedintention modulation, the performance of the conventional models in
1 https://huggingface.co/meta-llama/Meta-Llama-3-8B TABLE II: Predictive performance of DisasterMobLLM constructed upon different base models in different scenarios, with relative performance differences in terms of different indicators compared to the normal scenarios shown in parentheses. Normal Scenario Disaster Scenario Disaster Scenario with Intention Modulation
MULCONCAT ATTN
Model Acc@1 Acc@10 MRR Acc@1 Acc@10 MRR Acc@1 Acc@10 MRR Acc@1 Acc@10 MRR Acc@1 Acc@10 MRR
LSTM 0.0607 0.1527 0.0820 0.0181 0.0892 0.0436 0.0581 0.1293 0.0651 0.0626 0.1688 0.0879 0.0640 0.1597 0.0884
(- - -) (- - -) (- - -) (- - -) (- - -)
GRU 0.0307 0.1527 0.0786 0.0167 0.0649 0.0359 0.0226 0.0975 0.0601 0.0241 0.0953 0.0575 0.0313 0.1595 0.0738
(- - -) (- - -) (- - -)
0.1521 0.3129 0.1333
0.2799 0.4981 0.3004
0.0181 0.0892 0.0436
0.0167 0.0649 0.0359
0.1354 0.2796 0.1832
0.0854 0.1143 0.1167
0.1872 0.3198 0.2187
0.0581 0.1293 0.0651
0.0226 0.0975 0.0601
0.2523 0.4018 0.2695
0.1534 0.3154 0.1354
0.2761 0.4909 0.2981
0.0626 0.1688 0.0879
GRU 0.0307 0.1527 0.0786 0.0167 0.0649 0.0359 0.0226 0.0975 0.0601 0.0241 0.0953 0.0575 0.0313 0.1595 0.0738
(- - -) (- - -) (- - -)
DeepMove 0.2552 0.4902 0.2864 0.1354 0.2796 0.1832 0.2523 0.4018 0.2695 0.2141 0.4537 0.2294 0.2587 0.4982 0.2905 (- - -) (- - -) (- - -)
0.0241 0.0953 0.0575
0.2141 0.4537 0.2294
DeepMove 0.2552 0.4902 0.2864 0.1354 0.2796 0.1832 0.2523 0.4018 0.2695 0.2141 0.4537 0.2294 0.2587 0.4982 0.2905 (- - -) (- - -) (- - -)
Flashback 0.1576 0.3287 0.1342 0.0854 0.1143 0.1167 0.1534 0.3154 0.1354 0.1521 0.3129 0.1333 0.1579 0.3342 0.1398
(- - -) (- - -) (- - -)
Flashback 0.1576 0.3287 0.1342 0.0854 0.1143 0.1167 0.1534 0.3154 0.1354 0.1521 0.3129 0.1333 0.1579 0.3342 0.1398
(- - -) (- - -) (- - -)
STiSAN 0.2891 0.5107 0.3198 0.1872 0.3198 0.2187 0.2761 0.4909 0.2981 0.2799 0.4981 0.3004 0.2897 0.5118 0.3209
(- - -) (- - -) (- - -)
0.2897 0.5118 0.3209
TABLE III: The predictive performance of our model after removing different modules.
The best performance is given in bold. Model Acc@10 MRR Pre@Immob Rec@Immob F1@Immob
w/o RAG 0.3148 0.2691 0.3312 0.3187 0.3248 w/o Soft-prompt 0.3387 0.2770 0.3568 0.3248 0.3400 w/o Immobility 0.2896 0.2226 0.2197 0.2613 0.2387 w/o LLM Refining 0.3166 0.2531 0.2769 0.2903 0.2576 Ours 0.5118 0.3209 0.5012 0.5114 0.5062
TABLE IV: Comparison of intention prediction in normal ( Norm.
) and disaster ( Dis. ) scenarios. Scenarios Acc Pre@Immob Rec@Immob F1@Immob
Norm. 0.693 0.472 0.491 0.481
Dis. w/o LLM 0.438 0.418 0.428 0.422
Dis. 0.763 0.719 0.774 0.745
Intention Prediction Accuracy To evaluate the performance of the intention translator and predictor and the performance of LLM refinement at intention level, we compare the performance of intention prediction in both normal and disaster scenarios. The results are shown in Table IV, where the intention prediction accuracy will decrease in disaster scenarios by 58.2% in terms of Acc. In addition, the prediction of immobility intention also decreases when the scenario shifts from normal to disaster. Nevertheless, with the help of powerful LLMs, our method shows better performance in intention prediction even in disaster scenarios while achieving desirable performance in immobility prediction, with enhancements in Acc@1 by up to 10.1%. Intention Distribution in Different Cities and Scenarios
To further explore how our model can successfully transfer mobility knowledge from different cities and disasters by modeling at the intention level, we compare the intention distribution between different cities and disasters. The results
Intention-CLIP Analysis To evaluate that our Intention-CLIP can train the intention translator and predictorto learn the intention knowledge in the alignment process, we select both intention-related words (i.e., ”go” and ”travel”) andintentionunrelated words to compare top 5 weights for different prototypes. Intuitively, the linear layer between vocabularies and prototypes can be regarded as a matrix, which indicates how much a vocabulary contributes to different prototypes.
Thus, we select the top-5 contributions of intention-related andintention-not-related vocabularies. The results in Fig. 5 show that the top-5 intention-related vocabularies have higher weights than those intention-unrelated vocabularies. Moreover, the top-1 vocabulary’s weight is significantly higherthan the weight of the second-top vocabulary, showing that the Intention-CLIP can also distinguish different intentions reasonably. when a disaster occurs. Meanwhile, the intention distribution all disaster scenarios and normal scenarios in history. Forthe between different cities is relatively smaller in both normal given trajectory S 0: [D] T − 1 [and the disaster level] [ d] [, we retrieve] and disaster scenarios. F. Ablation Study
We also conduct an ablation study to quantify how each module of DisasterMobLLM affects its overall performance. In the w/o RAG variant, we remove the retrieval part for reference trajectories, indicating that the model will predict without external knowledge. In the w/o Soft-Prompt variant, we remove the soft prompt, indicating that the LLM can only acquire disaster information from the prompt in a few-shot learning fashion. We remove the special design of immobility in the w/o Immobility variant. In the w/o LLM Refining variant, we remove the LLM-based Intention Refiner and directly use the intention embedding predicted by the Intention Translator and Predictor fine-tuned on the disaster dataset as the modulation of the base model. The experimental results in Table III indicate that deleting any module results in a performance decrease, highlighting the necessity of each module to accurately predict mobility in disaster scenarios. VI. CONCLUSION
In this paper, we propose a mobility prediction model in a disaster scenario using cross-city knowledge and specially designed at the intention level. Extensive experiments illustrate that DisasterMobLLM can compensate forthe performance decrease in disaster scenarios of normal scenario mobility prediction models. Future directions can considertesting our model on more kinds of disasters, including epidemics, wildfires, etc. It is also possible to consider how government policies affect the mobility patterns in disaster scenarios, such as movement restriction and evacuation intervention.