Urban Mobility Prediction Using Twitter
| Autor: | Saeed Khan, Ash Rahimi, Neil W. Bergmann |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Transportation planning Emergency management Computer science business.industry 05 social sciences Mobility prediction 02 engineering and technology Data science Infrastructure management Work (electrical) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 020201 artificial intelligence & image processing Predictability business |
| Zdroj: | DASC/PiCom/CBDCom/CyberSciTech |
| DOI: | 10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00082 |
| Popis: | The characteristics and dynamics of human mobility have vital implications in areas such as disaster management, transportation planning and infrastructure management. While aggregate mobility modeling is useful for getting a broader overview of the system, the prediction of future movements of people in urban areas is also of significance. This work investigates the individual-level mobility of Twitter users in three Australian cities using the concepts of entropy and predictability. Twitter users are distinguished on the basis of their movement patterns and two distinct groups are identified. The randomness and regularity in their movements are calculated via multiple metrics, and prediction for the most active users in these cities is also performed. The top 10% of Brisbane users have 76.6% prediction accuracy, much higher than the other cities, suggesting heterogeneity among various cities. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|