# CrowdWeb: A Visualization Tool for Mobility Patterns in Smart Cities

arXiv: https://arxiv.org/abs/2305.12935 · HTML: https://mlsanity.com/paper/2305.12935/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 11 of 11)

- [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)
- [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)
- [Maximizing Consumer Satisfaction of IoT Energy Services](https://mlsanity.com/paper/2209.03619/) (2022)
- [Service-Based Wireless Energy Crowdsourcing](https://mlsanity.com/paper/2210.15494/) (2022)
- [Towards peer-to-peer sharing of wireless energy services](https://mlsanity.com/paper/2211.12464/) (2022)
- [Monitoring Efficiency of IoT Wireless Charging](https://mlsanity.com/paper/2303.05629/) (2023)
- [IMAP: Individual huMAn mobility Patterns visualizing platform](https://mlsanity.com/paper/2209.03615/) (2022)

## Abstract (arXiv)

Human mobility patterns refer to the regularities and trends in the way people move, travel, or navigate through different geographical locations over time. Detecting human mobility patterns is essential for a variety of applications, including smart cities, transportation management, and disaster response. The accuracy of current mobility prediction models is less than 25%. The low accuracy is mainly due to the fluid nature of human movement. Typically, humans do not adhere to rigid patterns in their daily activities, making it difficult to identify hidden regularities in their data. To address this issue, we proposed a web platform to visualize human mobility patterns by abstracting the locations into a set of places to detect more realistic patterns. However, the platform was initially designed to detect individual mobility patterns, making it unsuitable for representing the crowd in a smart city scale. Therefore, we extend the platform to visualize the mobility of multiple users from a city-scale perspective. Our platform allows users to visualize a graph of visited places based on their historical records using a modified PrefixSpan approach. Additionally, the platform synchronizes, aggregates, and displays crowd mobility patterns across various time intervals within a smart city. We showcase our platform using a real dataset.