An Improvement of the Arrival Time Estimation of an EV System Using Hybrid Approach with ANN

In this research, an approach for estimating the travelling time used by an electric vehicle and selecting an updating period of such vehicle to a particular location are proposed. The real-time based and historical data based techniques are used with Artificial Neural Network (ANN) as a process for memorizing the offset for estimating the vehicle velocity and updating period in the following round. The route of the vehicle, the time of the day, and the day of the week are taken into account. The proposed approach is analyzed and compared to the conventional approach by testing with the data (time and position of the vehicle) collected from running the vehicle around Naresuan University campus. The data was recorded every 1 second for 3 months using the wireless transmitter installed in the vehicle. From the results, it is found that, using the proposed approach, the bandwidth utilization of the network and the error of the displayed time are improved by 75%. With this significant improvement, if the proposed approach is further developed or utilized, the public vehicle service’s reliability could be increased; thus, less number of private vehicles utilized; resulting in a good environment saving

Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information