Longitude and Latitude Prediction Using ARIMA

The prediction of human and vehicle mobility in a city is becoming an attracting field. This topic attracts researchers from broad fields including behavioral sciences, where understanding the complexity of the human mobility behavior is one of the hot topic, and also to the industrial partners, who apply such results to many beneficial applications. Recent progress to sensing human mobility via smartphones is boosting this trend. However, due to the complexity and context-dependence of human behavior and the incompleteness and noise of geospatial data collecting from various sensors, the prediction of human and vehicle mobility is still far from solved. This workshop aimed at collecting contributions on the cutting-edge studies in human mobility description, modeling, intelligent computational method which can advance the human and vehicle prediction research. Potential topics included, but were not limited to 1) The next location prediction of individual mobility, 2) The crowd or population mobility prediction, 3) Dynamics of pedestrians, 4) commute flow and migration flow, 5) Traffic congestion, road usage forecast and optimal vehicle routing, 6) Social event forecast using geospatial data, 7) Novel agent mobility simulators, and 8) Case studies of mobility estimation in academia as well as in industrial field.

Effective human mobility modeling and prediction constitute the core prerequisites for various location-based applications. To encourage research in this direction, the ACM SIGSPATIAL Cup 2025 posed the challenge of predicting human mobility trajectories from a sparse multi-city dataset. This paper presents our solution, MoBERT, a BERT-like model that adapts and leverages mobility semantics with additional direction and distance prediction, providing supplementary supervision signals for robust feature learning. MoBERT models are trained in stages through curriculum learning, where augmented trajectories are ordered by increasing mobility entropy for training with progressively increasing difficulty. The final score of our method is 0.14609, as measured by average GEO-BLEU distances across four cities. Finally, we analyze the results and discuss insights from our approach.

In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users 1 over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.

This paper describes a human mobility prediction system which build by KDDI Research, Inc. to submit for Human Mobility Prediction Challenge (HuMob Challenge) 2024. The system consists of 6 human mobility prediction subsystems: 5 transformer-based subsystems; LSTM (Long Short-Term Memory) based subsystem. The system selects the best prediction result out of 6 outputs. Since a task of HuMob Challenge 2024 is to predict human mobility of day 61 to 75 using given human mobility data of day 1 to 60, the selection logic is built by the prediction performance of day 45 to 60 data. Although the selection logic is based on the prediction results on closed data set, the selection thought to work precisely.

We propose CLIFT ( C ross-City Lif estyle Pattern T ransfer for Human Mobility Prediction), a novel framework that enhances human mobility prediction by integrating general lifestyle patterns shared across cities with city-specific mobility patterns. Accurate human mobility prediction in urban environments is critical for transportation planning, marketing strategies, and disaster response. However, most existing deep learning approaches use only single-city data and exhibit significant performance degradation in small cities with limited training data. These limitations motivate methods that jointly leverage cross-city behavioral patterns and city-specific mobility characteristics. CLIFT addresses this challenge through dual complementary encoders: one captures general lifestyle patterns shared across cities, and the other captures city-specific mobility patterns; their outputs are integrated with a Transformer-based mobility predictor (LP-BERT). This architecture enables the model to jointly capture cross-city transferable behavioral patterns and city-specific mobility characteristics. We evaluated the effectiveness of CLIFT through experiments on the multi-city human mobility dataset LYMob-4Cities, comparing its performance with both single-city and multi-city deep learning-based methods. On average, CLIFT improved GEOBLEU and Top-1 accuracy by 11.1% and 10.6% over the single-city baseline, and by 5.0% and 7.9% over the multi-city baseline, respectively. Furthermore, CLIFT outperformed the top-ranked teams in the international competition, Human Mobility Prediction Challenge 2024 , demonstrating superior predictive performance under the same dataset and task setting.

The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility.

Human mobility prediction is a challenging and crucial task for fostering wide spectrum of location based applications, namely, traffic planning, travel-plan recommendation etc. However, modeling human movement behaviour and predicting next location are non-trivial tasks. We consider the human mobility pattern modeling and predicting next location problem for semantic trajectory data, wherein sparse GPS records are associated with other contextual information. Intuitively, human moves with an intent and the confluence of movement history (GPS traces) and contextual information may help in capturing people's intents at a fine granularity and has potential to improve the efficacy of location prediction problem. In this paper, a hierarchical and layered hidden markov model (HMM) framework for mobility prediction from sparse trajectories has been proposed. Experiment on real-life mobility dataset of Nokia Mobile Data Challenge (MDC) demonstrates the efficacy of the proposed framework even when with sparse GPS traces.

Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on human mobility prediction has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that human behavior can be highly predictable. Whether high predictability manifests itself for different modes of device usage, across spatial and temporal dimensions is still debatable. Despite existing studies, more investigations are needed to capture intrinsic mobility characteristics constraining predictability, to explore more dimensions (e.g. device types) and spatiotemporal granularities, especially with the change in human behavior and technology. We investigate practical predictability of next location visitation across three different dimensions: device type, spatial granularity and temporal spans using an extensive longitudinal dataset, with fine spatial granularity (AP level) covering 16 months. The study reveals device type as an important factor affecting predictability. Ultra-portable devices such as smartphones have "on-the-go" mode of usage (and hence dubbed "Flutes"), whereas laptops are "sit-to-use" (dubbed "Cellos"). The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types. We apply our systematic analysis to wireless traces from a large university campus. We compare several algorithms using varying degrees of temporal and spatial granularity for the two modes of devices; Flutes vs. Cellos. Through our analysis, we quantify how the mobility of Flutes is less predictable than the mobility of Cellos. In addition, this pattern is consistent across various spatio-temporal granularities, and for different methods (Markov chains, neural networks/deep learning, entropy-based estimators). This work substantiates the importance of predictability as an essential aspect of human mobility, with direct application in predictive caching, user behavior modeling and mobility simulations.

BackgroundHuman mobility behaviour modelling plays an essential role in the understanding and control of the spread of contagious diseases by limiting the contact among individuals, predicting the spatio-temporal evolution of an epidemic and inferring migration patterns. It informs programmatic and policy decisions for effective and efficient intervention. The objective of this research is to assess the human mobility pattern and analyse its implication for malaria disease epidemiology.MethodsIn this study, human mobility patterns in Benishangul-Gumuz and Gambella regions in Western Ethiopia were explored based on a cellular network mobility parameter (e.g., handover rate) via real world data. Anonymized data were retrieved for mobile active users with mobility related information. The data came from anonymous traffic records collected from all the study areas. For each cell, the necessary mobility parameter data per hour, week and month were collected. A scale factor was computed to change the mobility parameter value to the human mobility pattern. Finally, the relative human mobility probability for each scenario was estimated. MapInfo and Matlab softwares were used for visualization and analysis purposes. Hourly travel patterns in the study settings were compared with hourly malaria mosquito vector feeding behaviour.ResultsHeterogeneous human movement patterns were observed in the two regions with some areas showing typically high human mobility. Furthermore, the number of people entering into the two study regions was high during the highest malaria transmission season. Two peaks of hourly human movement, 8:00 to 9:00 and 16:00 to 18:00, emerged in Benishangul-Gumuz region while 8:00 to 10:00 and 16:00 to 18:00 were the peak hourly human mobility time periods in Gambella region. The high human movement in the night especially before midnight in the two regions may increase the risk of getting mosquito bite particularly by early biters depending on malaria linked human behaviour of the population.ConclusionsHigh human mobility was observed both within and outside the two regions. The population influx and efflux in these two regions is considerably high. This may specifically challenge the transition from malaria control to elimination. The daily mobility pattern is worth considering in the context of malaria transmission. In line with this malaria related behavioural patterns of humans need to be properly addressed.

Mobility prediction plays important roles in many fields. For example, tourist companies would like to know the characteristics of their customer movements so that they could design appropriate advertising strategies; sociologists has made many research on migration to try to find general features in human mobility; polices also analyze human movement behaviors to seek criminals. Thus, for location-based social networks, mobility prediction is an important task. This study proposes a mobility prediction model, which can be used to predict the user (human) mobility. The proposed approach is conducted from three characteristics: (1) regular movement in human mobility, (2) the influence of relationships on social networks, (3) other features (in this work, we consider “hot regions” where attract more people coming to there). To validate the proposed approach, three datasets including over 500,000 check-ins which are collected from two location-based social networks, namely Brightkite and Gowalla, are used for the experiments. Results show that the proposed model significantly improves the prediction accuracy, thus, this approach could be promising for mobility prediction, especially for location-based social networks.

Higher Education is facing disruptive innovation that requires, among other things, provision of a more effective and customized education service to individual students. In the field of Learning Analytics (LA), there has been much effort, in the form ...

Human mobility prediction plays a critical role in applications such as disaster response, urban planning, and epidemic forecasting. Traditional methods often rely on designing crafted, domain-specific models, and typically focus on short-term predictions, which struggle to generalize across diverse urban environments. In this study, we introduce Llama-3-8B-Mob, a large language model fine-tuned with instruction tuning, for long-term citywide mobility prediction---in a Q&A manner. We validate our approach using large-scale human mobility data from four metropolitan areas in Japan, focusing on predicting individual trajectories over the next 15 days. The results demonstrate that Llama-3-8B-Mob excels in modeling long-term human mobility---surpassing the state-of-the-art on multiple prediction metrics. It also displays strong zero-shot generalization capabilities---effectively generalizing to other cities even when fine-tuned only on limited samples from a single city. Source codes are available at https://github.com/TANGHULU6/Llama3-8B-Mob.

Predicting human mobility between locations has practical applications in transportation science, spatial economics, sociology and many other fields. For more than 100 years, many human mobility prediction models have been proposed, among which the gravity model analogous to Newton’s law of gravitation is widely used. Another classical model is the intervening opportunity (IO) model, which indicates that an individual selecting a destination is related to both the destination’s opportunities and the intervening opportunities between the origin and the destination. The IO model established from the perspective of individual selection behavior has recently triggered the establishment of many new IO class models. Although these IO class models can achieve accurate prediction at specific spatiotemporal scales, an IO class model that can describe an individual’s destination selection behavior at different spatiotemporal scales is still lacking. Here, we develop a universal opportunity model that considers two human behavioral tendencies: one is the exploratory tendency, and the other is the cautious tendency. Our model establishes a new framework in IO class models and covers the classical radiation model and opportunity priority selection model. Furthermore, we use various mobility data to demonstrate our model’s predictive ability. The results show that our model can better predict human mobility than previous IO class models. Moreover, this model can help us better understand the underlying mechanism of the individual’s destination selection behavior in different types of human mobility.

Rapidly developing location acquisition technologies provide a powerful tool for understanding and predicting human mobility in cities, which is very significant for urban planning, traffic regulation, and emergency management. However, with the existing methodologies, it is still difficult to accurately predict millions of peoples’ mobility in a large urban area such as Tokyo, Shanghai, and Hong Kong, especially when collected data used for model training are often limited to a small portion of the total population. Obviously, human activities in city are closely linked with point-of-interest (POI) information, which can reflect the semantic meaning of human mobility. This motivates us to fuse human mobility data and city POI data to improve the prediction performance with limited training data, but current fusion technologies can hardly handle these two heterogeneous data. Therefore, we propose a unique POI-embedding mechanism, that aggregates the regional POIs by categories to generate an artificial POI-image for each urban grid and enriches each trajectory snippet to a four-dimensional tensor in an analogous manner to a short video. Then, we design a deep learning architecture combining CNN with LSTM to simultaneously capture both the spatiotemporal and geographical information from the enriched trajectories. Furthermore, transfer learning is employed to transfer mobility knowledge from one city to another, so that we can fully utilize other cities’ data to train a stronger model for the target city with only limited data available. Finally, we achieve satisfactory performance of human mobility prediction at the citywide level using a limited amount of trajectories as training data, which has been validated over five urban areas of different types and scales.

Human Mobility Data in the COVID-19 Pandemic: Characteristics, Applications, and Challenges