# IMAP: Individual huMAn mobility Patterns visualizing platform

arXiv: https://arxiv.org/abs/2209.03615 · HTML: https://mlsanity.com/paper/2209.03615/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Understanding Urban Human Mobility through Crowdsensed Data](https://mlsanity.com/paper/1805.00628/) (2018)
- [Fairness-aware Crowdsourcing of IoT Energy Services](https://mlsanity.com/paper/2111.06064/) (2021)
- [Wireless IoT Energy Sharing Platform](https://mlsanity.com/paper/2201.11275/) (2022)
- [Quality of Experience Optimization in IoT Energy Services](https://mlsanity.com/paper/2208.13506/) (2022)
- [Toward Understanding Crowd Mobility in Smart Cities through the Internet of Things](https://mlsanity.com/paper/1909.08522/) (2019)
- [Understanding Crowd Behaviors in a Social Event by Passive WiFi Sensing and Data Mining](https://mlsanity.com/paper/2002.04401/) (2020)
- [Proactive Composition of Mobile IoT Energy Services](https://mlsanity.com/paper/2107.12519/) (2021)

## Built on by (most cited first, 1 of 1)

- [CrowdWeb: A Visualization Tool for Mobility Patterns in Smart Cities](https://mlsanity.com/paper/2305.12935/) (2023)

## Abstract (arXiv)

Understanding human mobility is essential for the development of smart cities and social behavior research. Human mobility models may be used in numerous applications, including pandemic control, urban planning, and traffic management. The existing models' accuracy in predicting users' mobility patterns is less than 25%. The low accuracy may be justified by the flexible nature of the human movement. Indeed, humans are not rigid in their daily movement. In addition, the rigid mobility models may result in missing the hidden regularities in users' records. Thus, we propose a novel perspective to study and analyze human mobility patterns and capture their flexibility. Typically, the mobility patterns are represented by a sequence of locations. We propose to define the mobility patterns by abstracting these locations into a set of places. Labeling these locations will allow us to detect close-to-reality hidden patterns. We present IMAP, an Individual huMAn mobility Patterns visualizing platform. Our platform enables users to visualize a graph of the places they visited based on their history records. In addition, our platform displays the most frequent mobility patterns computed using a modified PrefixSpan approach.