DBscan Algorithm and Decision Tree to Automate Trip Purpose Detection

One of transportation research topic is detecting trip purpose. Given a collection of GPS mobility records, researchers endeavored to infer useful information such as trip, travel mode, and trip purpose. Obtaining these attributes will help researcher in transportation modelling.  This work proposed an approach in defining a trip or a trip segmentation which is a part of trip purpose problem as well as inferring the trip purpose. By Utilizing Dbscan clustering algorithm, decision tree, and some useful features, we are able to detect the trips and their purposes as well as building the model to automate the trip derivation

Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information

Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields such as urban planning, transportation optimization and location-based marketing. Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic. In this paper, we propose \textsf{DMPP} (Deep Mixture Point Processes), a point process model for predicting spatio-temporal events with the use of rich contextual information; a key advance is its incorporation of the heterogeneous and high-dimensional context available in image and text data. Specifically, we design the intensity of our point process model as a mixture of kernels, where the mixture weights are modeled by a deep neural network. This formulation allows us to automatically learn the complex nonlinear effects of the contextual factors on event occurrence. At the same time, this formulation makes analytical integration over the intensity, which is required for point process estimation, tractable. We use real-world data sets from different domains to demonstrate that DMPP has better predictive performance than existing methods.Comment: KDD 1

Machine Learning for Identifying Group Trajectory Outliers

Prior works on the trajectory outlier detection problem solely consider individual outliers. However, in real-world scenarios, trajectory outliers can often appear in groups, e.g., a group of bikes that deviates to the usual trajectory due to the maintenance of streets in the context of intelligent transportation. The current paper considers the Group Trajectory Outlier (GTO) problem and proposes three algorithms. The first and the second algorithms are extensions of the well-known DBSCAN and kNN algorithms, while the third one models the GTO problem as a feature selection problem. Furthermore, two different enhancements for the proposed algorithms are proposed. The first one is based on ensemble learning and computational intelligence, which allows for merging algorithms’ outputs to possibly improve the final result. The second is a general high-performance computing framework that deals with big trajectory databases, which we used for a GPU-based implementation. Experimental results on different real trajectory databases show the scalability of the proposed approaches.acceptedVersio

Contextual contact tracing based spatio enhanced compartment modelling & spatial risk assessment

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe current situation of COVID-19 appears as a paradigm shift that seems to have farreaching impacts on the way humans will now continue with their daily routine. The overall scenario highlights the paramount importance of infectious disease surveillance, which necessitates immediate monitoring for effective preparedness and efficient response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. This thesis research investigates both requirements in a hybrid approach by the implementation of disease outbreak modelling and exploring its induced dynamic spatial risk in the form of Risk Assessment, along with its real-time integration back into the disease model. The study implements human mobility based contact tracing in the form of an event-based stochastic SIR model as a baseline and further modifies the existing setup to be inclusive of the spatial risk. This modification of each individual-level contact’s intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results suggest that the Spatio-SIR model tends to perform more meaningful events concerned with the Susceptible population rather than events to the Infected or Quarantined. With an example of a real-world scenario of induced spatial high-risk, it is highlighted that the new Spatio-SIR model can empower the analyst with a capability to explore disease dynamics from an additional perspective. The study concludes that even if this domain is hindered due to lack of data availability, the investigation process related to it should keep on exploring methods to effectively understand the disease dynamics

Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines.Comment: 8 pages, 9 figures, AAAI-202

Data Acquisition Applications

Data acquisition systems have numerous applications. This book has a total of 13 chapters and is divided into three sections: Industrial applications, Medical applications and Scientific experiments. The chapters are written by experts from around the world, while the targeted audience for this book includes professionals who are designers or researchers in the field of data acquisition systems. Faculty members and graduate students could also benefit from the book

Three Risky Decades: A Time for Econophysics?

Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era