Total: 2
With the rapid development of urbanization and public transportation system, the number of traffic accidents have significantly increased globally over the past decades and become a big problem for human society. Facing these possible and unexpected traffic accidents, understanding what causes traffic accident and early alarms for some possible ones will play a critical role on planning effective traffic management. However, due to the lack of supported sensing data, research is very limited on the field of updating traffic accident risk in real-time. Therefore, in this paper, we collect big and heterogeneous data (7 months traffic accident data and 1.6 million users' GPS records) to understand how human mobility will affect traffic accident risk. By mining these data, we develop a deep model of Stack denoise Autoencoder to learn hierarchical feature representation of human mobility. And these features are used for efficient prediction of traffic accident risk level. Once the model has been trained, our model can simulate corresponding traffic accident risk map with given real-time input of human mobility. The experimental results demonstrate the efficiency of our model and suggest that traffic accident risk can be significantly more predictable through human mobility.
This paper studies the impact of Time-Of-Use (TOU) tariffs in a competitive electricity market place. Specifically, it focuses on the question of how should an autonomous broker agent optimize TOU tariffs in a competitive retail market, and what is the impact of such tariffs on the economy. We formalize the problem of TOU tariff optimization and propose an algorithm for approximating its solution. We extensively experiment with our algorithm in a large-scale, detailed electricity retail markets simulation of the Power Trading Agent Competition (Power TAC) and: 1) find that our algorithm results in 15% peak-demand reduction, 2) find that its peak-flattening results in greater profit and/or profit-share for the broker and allows it to win against the 1st and 2nd place brokers from the Power TAC 2014 finals, and 3) analyze several economic implications of using TOU tariffs in competitive retail markets.