scikit-mobility: mobility analysis in Python

a PyTorch implementation of the paper "Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information"

Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility

[IEEE TITS 2024] Activity-aware human mobility prediction with hierarchical graph attention recurrent network.

This is a list of useful information about urban mobility prediction. Related papers, datasets and codes are included.

Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks

Urban Dynamics Through the Lens of Human Mobility

This repository contains the code for the paper "ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction".

a Python package designed to facilitate access to this mobility data and associated spatial tessellations, while standardizing the data for ease of use and analysis. As easy as pip install pyspainmobility

Analyzing and Visualizing Human Mobility Data in R

PyTorch implementation of the paper-"Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network"

No description provided.

Collect and filter location information from social network services.

Improving Next Location Prediction with Inferred Activity Semantics in Mobile Phone Data

Advances on human mobility science, covering the reading list of recent top academic conferences.

[NeurIPS 2025] RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility

A SLAW mobility simulator based on the OMNeT++ and INET frameworks

Collect and filter location information from social network services. (Web interface.)

Framework for modelling dynamical complex systems

sparkmobility: processing large human mobility dataset with Spark

sparkmobility-scala: Scala Spark codesapce for sparkmobility

Urban Informatics paper: Understanding internal migration in the UK before and during the COVID-19 pandemic using Twitter data

Fractional calculus and commercial air transport models used in: arxiv.org/abs/1601.07655

Framework to simulate the effect of the Braess Paradox on CO2 emissions in urban areas by modeling the traffic flow from real data and simulating it through SUMO.

Python code for the paper "LLMs are zero-shot next-location predictors" by Beneduce et al.

Mobility Network Embeddings for Multi-Dimensional Urban Segregation Analysis

This repository stores the required code to replicate the article "Using digital footprint data to monitor human mobility and support rapid humanitarian responses"

Code and data repository for paper titled "Fine-Scale Prediction of People's Home Location using Social Media Footprints"

This dataset born from the need of mobility traces provided with demographics data of the users and it allows to define several classes of users with their most relevant places. Using probability distributions, it can be used to generate slotted mobility traces for different users.

Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic