A dynamic approach to rebalancing bike-sharing systems

Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule

Networked Time Series Prediction with Incomplete Data

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks, which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%

ASSESSMENT OF SOLID WASTE MANAGEMENT IN TARKWA MUNICIPALITY GHANA: TIME SERIES APPROACH

This study intends to examine the means of waste disposal by households (place of disposal), analyze how the waste collected is finally disposed of, and predict the amount of waste that ends up at the landfill in the next five years using Time-Series and make recommendations for effective management of solid waste in Tarkwa Municipality. The historical data and the characteristic of the historical data show that the amount of waste generated in tonnes increased from year 2006 to 2011. The average waste generated was found to be 85612.8 tonnes. The graph of the projected waste using Time Series Method also showed an increase in the trend. It is observed that as the year progresses, there is increase in the amount of waste generated, the reason for this may not be far from increase in the population and urbanization of Tarkwa Municipality. Keywords: Waste, Landfill, Time-series method, Household, Disposal, Population, Urbanization,