251 research outputs found
D3P : Data-driven demand prediction for fast expanding electric vehicle sharing systems
The future of urban mobility is expected to be shared and electric. It is not only a more sustainable paradigm that can reduce emissions, but can also bring societal benefits by offering a more affordable on-demand mobility option to the general public. Many car sharing service providers as well as automobile manufacturers are entering the competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and to bring car sharing to the zero emissions level. During their fast expansion, one determinant for success is the ability of predicting the demand of stations as the entire system is growing continuously. There are several challenges in this demand prediction problem: First, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and decision support. Second, for the new stations to be deployed, there is no historical data available to help the prediction of their demand. Finally, the impact of deploying/closing stations on the other stations in the system can be complex. To address these challenges, we formulate the demand prediction problem in the context of fast expanding electric vehicle sharing systems, and propose a data-driven demand prediction approach which aims to model the expansion dynamics directly from the data. We use a local temporal encoding process to handle the historical data for each existing station, and a dynamic spatial encoding process to take correlations between stations into account with Graph Convolutional Neural Networks (GCN). The encoded features are fed to a multi-scale predictor, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach with real-world data collected from a major EV sharing platform for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the expanding EV sharing systems
Differential Recurrent Neural Networks for Human Activity Recognition
Human activity recognition has been an active research area in recent years. The difficulty of this problem lies in the complex dynamical motion patterns embedded through the sequential frames. The Long Short-Term Memory (LSTM) recurrent neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model various time-series data, where the current hidden state has to be considered in the context of the past hidden states. Unfortunately, the conventional LSTMs do not consider the impact of spatio-temporal dynamics corresponding to the given salient motion patterns, when they gate the information that ought to be memorized through time. To address this problem, we propose a differential gating scheme for the LSTM neural network, which emphasizes the change in information gain caused by the salient motions between the successive video frames. This change in information gain is quantified by Derivative of States (DoS), and thus the proposed LSTM model is termed differential Recurrent Neural Network (dRNN). Based on the energy profiling of DoS, we further propose to employ the State Energy Profile (SEP) to search for salient dRNN states and construct more informative representations. To better understand the scene and human appearance information, the dRNN model is extended by connecting Convolutional Neural Networks (CNN) and stacked dRNNs into an end-to-end model. Lastly, the dissertation continues to discuss and compare the combined and the individual orders of DoS used within the dRNN. We propose to control the LSTM gates via individual order of DoS and stack multiple levels of LSTM cells in increasing orders of state derivatives. To this end, we have introduced a new family of LSTMs, expanding the applications of LSTMs and advancing the performances of the state-of-the-art methods
Visual Crowd Analysis: Open Research Problems
Over the last decade, there has been a remarkable surge in interest in
automated crowd monitoring within the computer vision community. Modern
deep-learning approaches have made it possible to develop fully-automated
vision-based crowd-monitoring applications. However, despite the magnitude of
the issue at hand, the significant technological advancements, and the
consistent interest of the research community, there are still numerous
challenges that need to be overcome. In this article, we delve into six major
areas of visual crowd analysis, emphasizing the key developments in each of
these areas. We outline the crucial unresolved issues that must be tackled in
future works, in order to ensure that the field of automated crowd monitoring
continues to progress and thrive. Several surveys related to this topic have
been conducted in the past. Nonetheless, this article thoroughly examines and
presents a more intuitive categorization of works, while also depicting the
latest breakthroughs within the field, incorporating more recent studies
carried out within the last few years in a concise manner. By carefully
choosing prominent works with significant contributions in terms of novelty or
performance gains, this paper presents a more comprehensive exposition of
advancements in the current state-of-the-art.Comment: Accepted in AI Magazine published by Wiley Periodicals LLC on behalf
of the Association for the Advancement of Artificial Intelligenc
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