CoAcT: A framework for context-aware trip planning using active transport

Policy makers and urban planners around the world are encouraging people to use active transport by providing more easily accessible facilities for active transport users. However, trip planning using active transport is not straight forward and requires consideration of various trip contexts such as congestion, accessibility, attractiveness, safety as well as the physical ability of the traveller. The existing approaches do not provide a unified solution to integrate and represent these diverse set of contexts in active transport trip planning. In this paper, we propose a new framework called CoAcT which is able to integrate and represent various trip contexts for context aware trip planning using active transport. We also present two real world deployments of our proposed framework

Predicting imbalanced taxi and passenger queue contexts in airport

The taxi and passenger queue contexts indicate the various states of queues related to taxis and passengers (i.e. taxis are waiting for passengers, passengers are waiting for taxis, both are waiting for each other, none is waiting). Predicting these queue contexts in a future time is very important for better airport ground transport operations. However, queue context prediction at the airport is a challenging problem due to the presence of different contextual factors i.e., time, weather, taxi trips, flight arrivals and many more. Also these taxi and passenger queue contexts at the airport are imbalanced since some of the contexts are very infrequently occurring compared to others. In this paper, we address the problem of predicting imbalanced taxi and passenger queue contexts at the airport. First, we investigate different contextual factors, including time, taxi trips, passengers and weather for queue context prediction. Then we propose a detailed step by step solution to address this problem. To support the effectiveness of our detailed approach, we generate a queue context dataset by fusing three real world datasets including taxi trip, passenger wait time and weather condition that represent the taxi and passenger queue contexts at a major international airport in the New York City. The experimental results demonstrate that our developed queue context prediction framework provides detailed solutions to deliver higher accuracy in queue context prediction

CoAcT: A framework for context-aware trip planning using active transport

Policy makers and urban planners around the world are encouraging people to use active transport by providing more easily accessible facilities for active transport users. However, trip planning using active transport is not straight forward and requires consideration of various trip contexts such as congestion, accessibility, attractiveness, safety as well as the physical ability of the traveller. The existing approaches do not provide a unified solution to integrate and represent these diverse set of contexts in active transport trip planning. In this paper, we propose a new framework called CoAcT which is able to integrate and represent various trip contexts for context aware trip planning using active transport. We also present two real world deployments of our proposed framework

Queue context prediction using taxi driver knowledge

This paper addresses the problem of taxi-passenger queue context prediction using neighborhood based methods. We capture the taxi drivers' knowledge based on how they move in terms of temporal driver-knowledge deviation (TDKD). Then a TDKD-aided feature importance scheme is introduced for neighborhood based queue context prediction. We apply our proposed scheme to predict different queue contexts at a busy international airport in New York. We argue that the incorporation of taxi drivers' knowledge for calculating feature importance significantly improves the quality of selected neighborhood, thus boosting the prediction accuracy. The experimental results demonstrate the effectiveness of our proposed TDKD-aided feature importance scheme for neighborhood based taxi-passenger queue context prediction

Forecasting regional level solar power generation using advanced deep learning approach

Reliable integration of solar photovoltaic (PV) power into the electricity grid requires accurate forecasting at the regional level. While previous research has been primarily concerned with forecasting PV power output from a single plant, this research focuses on regional level forecasting which is more beneficial for economic operations of power systems. This paper presents an advanced deep learning-based approach, called CNNs-LSTM Encoder-Decoder (CLED), to predict the regional level aggregated PV power generation for the next day at half-hourly intervals. The proposed approach utilizes the ability of Convolutional Neural Networks (CNNs) to capture and learn the internal representation of intermittent time-series data. It also uses Long Short-Term Memory (LSTM) network for recognizing temporal dependencies in the data. The performance of the CLED model is evaluated using a large data set from the Australian Energy Market Operator (AEMO). Results demonstrate that CLED provides accurate predictions, outperforming baselines and state-of-the-art models in the literature

A blockchain-based architecture for integrated smart parking systems

In this paper, we introduce an integrated smart parking system. The proposed integrated smart parking system brings multiple parking service providers together under a unified platform aiming to provide one-stop parking information services to the commuters in a smart city. However, the adaptation of such a system is prone to tempering while a massive amount of data is shared among different parties which raise concerns related to trust and performance. To address this challenge, we propose a blockchain-based architecture specific to the integrated smart parking systems. Finally, we present a set of design principles which shows the applicability of our proposed blockchain-based integrated parking system

CoSEM: Contextual and semantic embedding for App usage prediction

App usage prediction is important for smartphone system optimization to enhance user experience. Existing modeling approaches utilize historical app usage logs along with a wide range of semantic information to predict the app usage; however, they are only effective in certain scenarios and cannot be generalized across different situations. This paper address this problem by developing a model called Contextual and Semantic Embedding model for App Usage Prediction (CoSEM) for app usage prediction that leverages integration of 1) semantic information embedding and 2) contextual information embedding based on historical app usage of individuals. Extensive experiments show that the combination of semantic information and history app usage information enables our model to outperform the baselines on three real-world datasets, achieving an MRR score over 0.55,0.57,0.86 and Hit rate scores of more than 0.71, 0.75, and 0.95, respectively

Spatially aggregated photovoltaic power prediction using wavelet and convolutional neural networks

Forecasting the power generation from intermittent renewable energy sources, such as Photovoltaic (PV) systems, is crucial for the reliable operations of power systems. In this paper, we consider the task of spatially aggregated PV power generation from large-scale, grid-connected and geographically dispersed PV sites. PV power generation data is highly uncertain, non-linear and non-stationary, making accurate forecasting very challenging. We present a new approach, Wavelet Convolutional Neural Networks (WCNNs), by combining Wavelet Transformation (WT) with Convolutional Neural Networks (CNNs). The WCNNs approach first applies time-invariant WT to decompose the highly fluctuating PV power time series into multiple components. It then predicts the approximation (i.e., low frequency smoothed time series) and details (i.e., high frequency random noise) using CNNs and linear regression, respectively. Extensive evaluation using a real dataset from the Australian Energy Market Operator (AEMO) shows that WCNNs is an effective approach and outperforms the state-of-the-art machine learning models both with and without WT

Forecasting regional level solar power generation using advanced deep learning approach

Reliable integration of solar photovoltaic (PV) power into the electricity grid requires accurate forecasting at the regional level. While previous research has been primarily concerned with forecasting PV power output from a single plant, this research focuses on regional level forecasting which is more beneficial for economic operations of power systems. This paper presents an advanced deep learning-based approach, called CNNs-LSTM Encoder-Decoder (CLED), to predict the regional level aggregated PV power generation for the next day at half-hourly intervals. The proposed approach utilizes the ability of Convolutional Neural Networks (CNNs) to capture and learn the internal representation of intermittent time-series data. It also uses Long Short-Term Memory (LSTM) network for recognizing temporal dependencies in the data. The performance of the CLED model is evaluated using a large data set from the Australian Energy Market Operator (AEMO). Results demonstrate that CLED provides accurate predictions, outperforming baselines and state-of-the-art models in the literature

A blockchain-based architecture for integrated smart parking systems

In this paper, we introduce an integrated smart parking system. The proposed integrated smart parking system brings multiple parking service providers together under a unified platform aiming to provide one-stop parking information services to the commuters in a smart city. However, the adaptation of such a system is prone to tempering while a massive amount of data is shared among different parties which raise concerns related to trust and performance. To address this challenge, we propose a blockchain-based architecture specific to the integrated smart parking systems. Finally, we present a set of design principles which shows the applicability of our proposed blockchain-based integrated parking system