# Research - Shen Liang | PhD Student at PolyU

> Research works by Shen Liang on human mobility, geospatial AI, and urban informatics, with publication summaries, abstracts, citations, and licensed full texts.

Source: <https://nehsgnail.github.io/research/>

## Curated List

A collection of his recent works.

![Predicting short-term urban bike sharing demand in a coupled continuous and network space](<https://raw.githubusercontent.com/nehSgnaiL/research-assets-archive/refs/heads/main/2026-TBS-GeoTopoNet/research-card-GeoTopoNet.png>)

### [Predicting short-term urban bike sharing demand in a coupled continuous and network space](<https://nehsgnail.github.io/research/2026-TBS-GeoTopoNet>)

S. Liang, Y. Xu, G. Li, X. Zhang, Q. Li

Travel Behaviour and Society, 2026

Distance tells only half the story of how a city moves. The true pulse of urban mobility is shaped just as much by the underlying transit networks. GeoTopo-Net offers a way to model both dependencies at once, capturing how travel unfolds across places and connections.

![Improving next location prediction with inferred activity semantics in mobile phone data](<https://raw.githubusercontent.com/nehSgnaiL/LPA/refs/heads/main/img/tjde_a_2552880_f0003_oc.jpg>)

### [Improving next location prediction with inferred activity semantics in mobile phone data](<https://nehsgnail.github.io/research/2025-IJDE-LPA>)

S. Liang, Q. Li, L. Zhuo, D. Zou, Y. Xu, S. Zhou

International Journal of Digital Earth, 2025

To predict where someone is headed, do we need to know why? This study shows that giving AI the "why" makes its predictions much sharper. We find that using a diverse mix of activities works better than sticking to a few safe, accurate categories, even if the specific guesses are imperfect.

![Assessing personal travel exposure to on-road PM2.5 using cellphone positioning data and mobile sensors](<https://raw.githubusercontent.com/nehSgnaiL/research-assets-archive/refs/heads/main/2022-H%26P-PM25/figure-9.jpg>)

### [Assessing personal travel exposure to on-road PM2.5 using cellphone positioning data and mobile sensors](<https://nehsgnail.github.io/research/2022-H&P-PM25>)

Q. Li, S. Liang, Y. Xu, L. Liu, S. Zhou

Health & Place, 2022

Does your commute hide a pollution risk? We measure it in Guangzhou by linking city-wide travel data with mobile sensors, and uncover three distinct patterns based on how and when people travel.