A review on recent sizing methodologies of hybrid renewable energy systems

Recent progress in Artificial Intelligence (AI) techniques, the large-scale availability of high-quality data, as well as advances in both hardware and software to efficiently process these data, a...

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2019.1684500

GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond

A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-007155.pdf

Limits of predictability in human mobility

In recent years, various deep learning architectures have been proposed to solve complex challenges (e.g. spatial dependency, temporal dependency) in traffic domain, which have achieved satisfactory performance. These architectures are composed of multiple deep learning techniques in order to tackle various challenges in traffic tasks. Traditionally, convolution neural networks (CNNs) are utilized to model spatial dependency by decomposing the traffic network as grids. However, many traffic networks are graph-structured in nature. In order to utilize such spatial information fully, it's more appropriate to formulate traffic networks as graphs mathematically. Recently, various novel deep learning techniques have been developed to process graph data, called graph neural networks (GNNs). More and more works combine GNNs with other deep learning techniques to construct an architecture dealing with various challenges in a complex traffic task, where GNNs are responsible for extracting spatial correlations in traffic network. These graph-based architectures have achieved state-of-the-art performance. To provide a comprehensive and clear picture of such emerging trend, this survey carefully examines various graph-based deep learning architectures in many traffic applications. We first give guidelines to formulate a traffic problem based on graph and construct graphs from various kinds of traffic datasets. Then we decompose these graph-based architectures to discuss their shared deep learning techniques, clarifying the utilization of each technique in traffic tasks. What's more, we summarize some common traffic challenges and the corresponding graph-based deep learning solutions to each challenge. Finally, we provide benchmark datasets, open source codes and future research directions in this rapidly growing field.

How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

To mitigate congestion caused by the increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. With a better understanding of the travel patterns and regularity (the “magnitude” level of travel pattern) of transit riders, transit authorities can evaluate the current transit services to adjust marketing strategies, keep loyal customers and improve transit performance. However, it is fairly challenging to identify travel pattern for each individual transit rider in a large dataset. Therefore, this paper proposes an efficient and effective data-mining approach that models the travel patterns of transit riders using the smart card data collected in Beijing, China. Transit riders’ trip chains are identified based on the temporal and spatial characteristics of smart card transaction data. Based on the identified trip chains, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to detect each transit rider’s historical travel patterns. The K-Means++ clustering algorithm and the rough-set theory are jointly applied to clustering and classifying the travel pattern regularities. The rough-set-based algorithm is compared with other classification algorithms, including Naive Bayes Classifier, C4.5 Decision Tree, K-Nearest Neighbor (KNN) and three-hidden-layers Neural Network. The results indicate that the proposed rough-set-based algorithm outperforms other prevailing data-mining algorithms in terms of accuracy and efficiency.

Mining Smart Card Data for Transit Riders’ Travel Patterns

Mode choice, substitution patterns and environmental impacts of shared and personal micro-mobility

/pdf/the-surprising-efficiency-of-framing-geo-spatial-time-series-3y5xqqs205.pdf

The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task - Insights from the IARAI Traffic4cast Competition at NeurIPS 2019.

Modeling the optimal mix and location of wind and solar with transmission and carbon pricing considerations

This paper presents a market model for the EPEX SPOT German continuous intraday market for electric power trading based on the limit order book (LOB). We use the EPEX SPOT M7 order book data, which contains all orders submitted to the German continuous intraday market, to simulate the historic course of the market. Thereby, we reconstruct the complete state of the LOB at every point in (trading) time. We validate our simulation by comparing the transactions that our simulation generated with the actual historical transactions available from a different data set. The LOB based market model can be used to include price volatility risk and illiquidity risk when simulating trading at the EPEX SPOT continuous intraday market. Furthermore, we present all preprocessing steps and decision rules necessary to correctly identify orders from the often ambiguous EPEX SPOT M7 order book data.

German Intraday Electricity Market Analysis and Modeling Based on the Limit Order Book

/pdf/graph-convolutional-neural-networks-for-human-activity-39gn26d60j.pdf

Henry Martin

Papers

Mode choice, substitution patterns and environmental impacts of shared and personal micro-mobility

The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task - Insights from the IARAI Traffic4cast Competition at NeurIPS 2019.

Modeling the optimal mix and location of wind and solar with transmission and carbon pricing considerations

German Intraday Electricity Market Analysis and Modeling Based on the Limit Order Book

Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data