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 …

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

PyTorch implementation of "HGARN: Hierarchical Graph Attention Recurrent Network for Human Mobility Prediction".

Urban Dynamics Through the Lens of Human Mobility

Human mobility data (in form of <x,y,t>) analysis and visualization in R.

Collect and filter location information from social network services.

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

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

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

A Python library for computing several metrics related to predictability in human mobility

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

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

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".

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"

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.

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.