A modified multilevel k-way partitioning algorithm for trip-based road networks

In today’s world, the traffic volume on urban road networks is multiplying rapidly due to the heavy usage of vehicles and mobility on demand services. Migration of people towards urban areas result in increasing size and complexity of urban road networks. When handling such complex traffic systems, partitioning the road network into multiple sub-regions and managing the identified sub regions is a popular approach. In this paper, we propose an algorithm to identify sub-regions of a road network that exhibit homogeneous traffic flow patterns. In a stage wise manner, we model the road network graph by using taxi-trip data obtained on the selected region. Then, we apply the proposed modified multilevel kway partitioning algorithm to obtain optimal number of partitions from the developed road graph. An interesting feature of this algorithm is, resulting partitions are geographically connected and consists minimal interpartition trip flow. Our results show that the proposed algorithm outperforms state-of-the-art multilevel partitioning algorithms for tripbased road networks. By this research, we demonstrate the ability of road network partitioning using trip data while preserving the partition homogeneity and connectivity

ACTSEA : annotated corpus for Tamil & Sinhala emotion analysis

The purpose of text emotion analysis is to detect and recognize the classification of feeling expressed in text. In recent years, there has been an increase in text emotion analysis studies for English language since data were abundant. Due to the growth of social media large amount data are now available for regional languages such as Tamil and Sinhala as well. However, these languages lack necessary annotated corpus for many NLP tasks including emotion analysis. In this paper, we present our scalable semi-automatic approach to create an annotated corpus named ACTSEA for Tamil and Sinhala to support emotion analysis. Alongside, our analysis on a sample of the produced data and the useful findings are presented for the low resourced NLP community to benefit. For ACTSEA, data were gathered from twitter platform and annotated manually after cleaning. We collected 600280 (Tamil) and 318308 (Sinhala) tweets in total which makes our corpus largest data collection which is currently available for these languages

Video colorization dataset and benchmark

Video colorization is the process of assigning realistic, plausible colors to a grayscale video. Compared to its peer, image colorization, video colorization is a relatively unexplored area in computer vision. Most of the models available for video colorization are extensions of image colorization, and hence are unable to address some unique issues in video domain. In this paper, we evaluate the applicability of image colorization techniques for video colorization, identifying problems inherent to videos and attributes affecting them. We develop a dataset and benchmark to measure the effect of such attributes to video colorization quality and demonstrate how our benchmark aligns with human evaluations

Taxi trip travel time prediction with isolated XGBoost regression

Travel time prediction is crucial in developing mobility on demand systems and traveller information systems. Precise estimation of travel time supports the decision-making process for riders and drivers who use such systems. In this paper, static travel time for taxi trip trajectories is predicted by applying isolated XGBoost regression models to a set of identified inlier and extreme-conditioned trips and the results are compared with other existing best models in this context. XGBoost uses an ensemble of decision trees and is robust to outliers and thus it is believed to perform well on time series predictions. We show that, compared to other existing best models, XGB-IN (XGBoost prediction model of in-lier trips) model prediction values reduce mean absolute error as well as root mean squared error and exhibit impressive correlation with actual travel time values while XGB-Extreme model is able to provide reasonably accurate prediction results for a set of extreme-conditioned trips with shorter actual time durations. We demonstrate the achievability of travel time prediction with XGBoost regression and show that our approach is applicable to large-scale data and performs well in predicting static travel time

A modified multilevel k-way partitioning algorithm for trip-based road networks

In today’s world, the traffic volume on urban road networks is multiplying rapidly due to the heavy usage of vehicles and mobility on demand services. Migration of people towards urban areas result in increasing size and complexity of urban road networks. When handling such complex traffic systems, partitioning the road network into multiple sub-regions and managing the identified sub regions is a popular approach. In this paper, we propose an algorithm to identify sub-regions of a road network that exhibit homogeneous traffic flow patterns. In a stage wise manner, we model the road network graph by using taxi-trip data obtained on the selected region. Then, we apply the proposed modified multilevel kway partitioning algorithm to obtain optimal number of partitions from the developed road graph. An interesting feature of this algorithm is, resulting partitions are geographically connected and consists minimal interpartition trip flow. Our results show that the proposed algorithm outperforms state-of-the-art multilevel partitioning algorithms for tripbased road networks. By this research, we demonstrate the ability of road network partitioning using trip data while preserving the partition homogeneity and connectivity

A Building Height-Dependent Gaussian Mixture Model to Characterize Air-to-Ground Wireless Channels

With the continuous evolution of Unmanned Aerial Vehicles (UAVs) in terms of flight autonomy and high payload capabilities, many new applications have emerged recently. In this context, potential usage of UAVs has been explored in providing wireless communication service. However, our understanding of the wireless channels associated with UAVs is still in its infancy. Therefore, in this paper, we use ray-tracing simulations to develop a novel Gaussian Mixture Model (GMM) for Air-to-Ground (A2G) channels. An urban environment with mean building heights of 10m, 20m, 50m, and 80m is considered to develop the proposed model. An extensive set of simulations are performed using a ray-tracing simulator, Wireless InSite ® . Our results show that the Probability Density Function (PDF) of the received power or the path loss vary depending on the mean building height and can be modelled using a GMM. The proposed model is then validated by using it to generate PDFs of a certain test set of city environments