Name
Abstract | ||
|---|---|---|
Understanding urban human mobility is crucial for epidemic control, urban planning, traffic forecasting systems and, more recently, various mobile and network applications. Nowadays, a variety of urban human mobility data have been gathered and published. Pervasive GPS data can be collected by mobile phones. A mobile operator can track people's movement in cities based on their cellular network location. This urban human mobility data contains rich knowledge about locations and can help in addressing many urban challenges such as traffic congestion or air pollution problems. In this article, we survey recent literature on urban human mobility from a data mining view: from the data collection and cleaning, to the mobility models and the applications. First, we summarize recent public urban human mobility data sets and how to clean and preprocess such data. Second, we describe recent urban human mobility models and predictors, e.g., the deep learning predictor, for predicting urban human mobility. Third, we describe how to evaluate the models and predictors. We conclude by considering how applications can utilize the mobility models and predictive tools for addressing city challenges. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | human mobility, spatio-temporal data mining, machine learning, smart city |
Field | DocType | Citations |
Data mining,Data collection,Data modeling,Computer science,Mobility model,Public transport,Urban planning,Global Positioning System,Cellular network,Traffic congestion | Conference | 6 |
PageRank | References | Authors |
0.44 | 32 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Zhao | 1 | 89 | 6.70 |
| Sasu Tarkoma | 2 | 1312 | 125.76 |
| Siyuan Liu | 3 | 544 | 37.89 |
| Huy T. Vo | 4 | 1035 | 61.10 |