We've seen how social media can be more than just a place to share pictures of your dinner. It can play an important role in cultural movements, political discourse, tracking diseases and now, researchers have discovered that it can play a crucial role in natural disaster relief by predicting the true impact in just a few hours. An international study by researchers at the Universidad Carlos III de Madrid (UC3M), NICTA (National Information Communications Technology Australia) and the University of California in San Diego has found that analysis of social network activity during and in the hours following a natural disaster can quickly reveal the extent of damage. "Twitter, the social network which we have analyzed, is useful for the management, real-time monitoring and even prediction of the economic impact that disasters like Hurricane Sandy can have," says one of the researchers, Esteban Moro Egido, of UC3M. Hurricane Sandy was the perfect chance for the researchers to collect data because it was a very large storm that was being tracked and they could monitor Twitter for information before, during and after it hit areas. Hundreds of millions of geo-located tweets were sent by Twitter users referencing the storm in 50 metropolitan areas. The researchers were able to track the movement and impact of storm through Twitter activity as it pummeled the East Coast. The storm caused more damage than any other is U.S. history with an economic impact of 50 billion dollars. The researchers compared the Twitter data they collected with official FEMA data concerning the level of aid grants for different areas. The researchers found that there was a strong correlation between the mean per capita social network activity and the mean per capita economic damage for each area. The danger and actual disaster impact was directly observable in real time by monitoring the social network. The researchers have gone on to verify that the same correlation exists in floods, tornados and storms. The researchers believe that social networks could be a critical prediction tool for the damage of natural disasters, giving governments the ability to see where and how much relief will be needed much more quickly. It can also be used to see where people are in need of immediate help so that first responders can be dispatched to the hardest hit areas. The researchers say this finding is especially important as we face an increase in natural disasters due to climate change. "We believe that this is going to cause even more natural disasters and, therefore, the use of social networks will allow us to obtain useful supplementary information," Egido said. "We are trying to see if there is a relationship between activity on social networks and climate change which will affect us in the future". If social networks are monitored, more lives could be saved and the right amount of aid will reach the areas that need it much more quickly.
The study, published in the latest issue of the journal Science Advances, along with scientists from NICTA (National Information Communications Technology Australia) and the University of California in San Diego concludes that it is possible to determine the damage caused by a natural disaster in just a few hours, by using data from social networks. "Twitter, the social network which we have analyzed, is useful for the management, real-time monitoring and even prediction of the economic impact that disasters like Hurricane Sandy can have," says one of the researchers, Esteban Moro Egido, of UC3M's Grupo Interdisciplinar de Sistemas Complejos - Complex Systems Interdisciplinary Group (GISC). The research was carried out by analyzing Twitter activity before, during and after Hurricane Sandy which, in 2012, caused more damage than any other storm in US history, with an economic impact in the region of 50,000 million dollars. Hundreds of millions of geo-located tweets making reference to this topic were collected from fifty metropolitan areas in the USA. "Given that citizens were turning to these platforms for communication and information related to the disaster, we established a strong correlation between the route of the hurricane and activity on social networks," explains Esteban Moro. But the main conclusion of the study was obtained when the data relating to social network activity was examined alongside data relating to both the levels of aid granted by the Federal Emergency Management Agency (FEMA) and insurance claims: there is a correlation between the mean per capita of social network activity and economic damage per capita caused by these disasters in the areas where such activity occurs. In other words, both real and perceived threats, along with the economic effects of physical disasters, are directly observable through the strength and composition of the flow of messages from Twitter. Furthermore, researchers have verified the results obtained from Hurricane Sandy and have been able to demonstrate that the same dynamic also occurs in the case of floods, storms and tornadoes; for example, whenever there is sufficient activity on social media to extract such data. In this way, communication on Twitter allows the economic impact of a natural disaster in the affected areas to be monitored in real time, making it possible to provide information in addition to that currently used to assess damage resulting from these disasters. Moreover, the distribution space of the event-related messages can also help the authorities in the monitoring and evaluation of emergencies, in order to improve responses to natural disasters. The authors of the study suggest that we are facing an increase in the frequency and intensity of natural disasters as a consequence of climate change. "We believe that this is going to cause even more natural disasters and, therefore, the use of social networks will allow us to obtain useful supplementary information," points out Professor Esteban Moro, who is currently working on further research in this area. "We are trying to see if there is a relationship between activity on social networks and climate change which will affect us in the future". Explore further: A system detects global trends in social networks two months in advance More information: Y. Kryvasheyeu, H. Chen, N. Obradovich, E. Moro, P. Van Hentenryck, J. Fowler, M. Cebrian, Rapid Assessment of Disaster Damage Using Social Media Activity. Sci. Adv. 2, e1500779 (2016) DOI: 10.1126/sciadv.1500779, http://advances.sciencemag.org/content/2/3/e1500779
Zhang Z.,National Information Communications Technology Australia |
Zhang Z.,University of Sydney |
Mao G.,National Information Communications Technology Australia |
Anderson B.D.O.,Australian National University
IEEE Transactions on Intelligent Transportation Systems | Year: 2014
This paper studies the information propagation process in wireless communication networks formed by vehicles traveling on a highway. Corresponding to different lanes of the highway and different types of vehicles, we consider that vehicles in the network can be categorized into a number of traffic streams, where the vehicles in the same traffic stream have the same speed distribution while the speed distributions of vehicles in different traffic streams are different. We analyze the information propagation process of the aforementioned vehicular network and obtain an analytical formula for the information propagation speed (IPS). Using the formula, one can straightforwardly study the impact of parameters such as radio range, vehicular traffic density, vehicular speed distribution, and the time variation of vehicular speed on the IPS. The accuracy of the analytical results is validated using simulations. © 2013 IEEE. Source
Duell M.,National Information Communications Technology Australia |
Duell M.,University of New South Wales |
Gardner L.M.,National Information Communications Technology Australia |
Gardner L.M.,University of New South Wales |
And 3 more authors.
Transportation Research Record | Year: 2014
Transport network pricing schemes are an integral traffic management strategy that can be implemented to reduce congestion, among other network impacts. However, the problem of determining tolls becomes much more complex when multiple sources of demand uncertainty are considered. This paper proposes a novel tolling model based on a particular variant of strategic user equilibrium in which users base their route choice decisions on a known demand distribution. The study showed that by using an average daily demand, a marginal social cost-based tolling approach could induce near optimal conditions in a strategic network. However, uncertainty was associated with the long-term future planning demand; inaccurate forecasts of future demand could result in poor realized tolling scheme performance. Therefore, this paper also proposes a method to test the robustness of a tolling scheme, which is the reliability of the link tolls under a range of future demand scenario realizations. Results demonstrated that evaluations of strategic tolling schemes differed when both the short-term and the long-term uncertainty in demand were accounted for, and furthermore suggested that future research into the integration of multiple sources of uncertainty into the pricing scheme evaluation is merited. Source