Clustering and Modeling of Network level Traffic States based on Locality Preservative Non-negative Matrix Factorization

International audienceIn this paper, we propose to cluster and model network-level traffic states based on a geometrical weighted similarity measure of network-level traffic states and locality preservative non-negative matrix factorization. The geometrical weighted similarity measure makes use of correlation between neighboring roads to describe spatial configurations of global traffic patterns. Based on it, we project original high-dimensional network-level traffic information into a feature space of much less dimensionality through the matrix factorization method. With the obtained low-dimensional representation of global traffic information, we can describe global traffic patterns and the evolution of global traffic states in a flexible way. The experiments prove validity of our method for the case of large-scale traffic network

Analysis of Large-scale Traffic Dynamics using Non-negative Tensor Factorization

International audienceIn this paper, we present our work on clustering and prediction of temporal dynamics of global congestion configurations in large-scale road networks. Instead of looking into temporal traffic state variation of individual links, or of small areas, we focus on spatial congestion configurations of the whole network. In our work, we aim at describing the typical temporal dynamic patterns of this network-level traffic state and achieving long-term prediction of the large-scale traffic dynamics, in a unified data-mining framework. To this end, we formulate this joint task using Non-negative Tensor Factorization (NTF), which has been shown to be a useful decomposition tools for multivariate data sequences. Clustering and prediction are performed based on the compact tensor factorization results. Experiments on large-scale simulated data illustrate the interest of our method with promising results for long-term forecast of traffic evolution

Large scale estimation of arterial traffic and structural analysis of traffic patterns using probe vehicles

International audienceEstimating and analyzing traffi c conditions on large arterial networks is an inherently diffi cult task. The fi rst goal of this article is to demonstrate how arterial tra c conditions can be estimated using sparsely sampled GPS probe vehicle data provided by a small percentage of vehicles. Traffi c signals, stop signs, and other flow inhibitors make estimating arterial traffi c conditions significantly more diffi cult than estimating highway traffi c conditions. To address these challenges, we propose a statistical modeling framework that leverages a large historical database and relies on the fact that tra ffic conditions tend to follow distinct patterns over the course of a week. This model is operational in North California, as part of the Mobile Millennium tra ffic estimation platform. The second goal of the article is to provide a global network-level analysis of tra ffic patterns using matrix factorization and clustering methods. These techniques allow us to characterize spatial tra ffic patterns in the network and to analyze traffi c dynamics at a network scale. We identify tra ffic patterns that indicate intrinsic spatio-temporal characteristics over the entire network and give insight into the traffi c dynamics of an entire city. By integrating our estimation technique with our analysis method, we achieve a general framework for extracting, processing and interpreting traffi c information using GPS probe vehicle data

Near-Lossless Compression for Large Traffic Networks

With advancements in sensor technologies, intelligent transportation systems can collect traffic data with high spatial and temporal resolution. However, the size of the networks combined with the huge volume of the data puts serious constraints on system resources. Low-dimensional models can help ease these constraints by providing compressed representations for the networks. In this paper, we analyze the reconstruction efficiency of several low-dimensional models for large and diverse networks. The compression performed by low-dimensional models is lossy in nature. To address this issue, we propose a near-lossless compression method for traffic data by applying the principle of lossy plus residual coding. To this end, we first develop a low-dimensional model of the network. We then apply Huffman coding (HC) in the residual layer. The resultant algorithm guarantees that the maximum reconstruction error will remain below a desired tolerance limit. For analysis, we consider a large and heterogeneous test network comprising of more than 18 000 road segments. The results show that the proposed method can efficiently compress data obtained from a large and diverse road network, while maintaining the upper bound on the reconstruction error.Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobility Program

Population genomic inference of ecology, conservation, evolution, and demographic history of Atlantic seahorses and pipefishes (Syngnathidae)

In the Atlantic Ocean powerful directional ocean currents can play a significant role in the formation and persistence of marine species. Syngnathidae fishes have a sparse fossil record, high morphological plasticity, and many of these species are difficult to observe in the wild, therefore they frequently lack life history information and the status of regional lineages and species designations are often obscure. In this dissertation I explore the ecology, evolution, and conservation of primarily Atlantic seahorses (Hippocampus) and pipefish (Syngnathus) in four core chapters, using differing genetic datasets ranging from mitochondrial DNA to genome-wide RAD sequences. Most Synganthids have the potential to disperse passively by rafting on floating vegetation, and are direct developers, which is thought to limit their active mobility, yet many species have widespread distributions. The majority of genetic research on Syngnathids fishes has focused on Indo-Pacific species, however the Atlantic Ocean is home to dozens of species of pipefishes from nine genera and roughly 1/5th of the world\u27s seahorses species. In Chapter 1, I use six loci to infer the species tree for all Atlantic seahorses and infer the demographic history and evolution of the Hippocampus erectus complex. The results of this study support the establishment of an ancestral population of the H. erectus complex in the Americas, followed by the Amazon River outflow splitting it into Caribbean/North American H. erectus and South American H. patagonicus at a time of increased sedimentation and outflow. Following this split, colonization occurred across the Atlantic via the Gulf Stream currents with subsequent trans-Atlantic isolation. Based on the results of Chapter 1, the species H. erectus exhibited a panmictic genetic structure from Latin America to temperate New York waters. However, inhabitants of the temperate region are considered by some ecologists to be tropical vagrants that only arrive during warm seasons from the southern provinces and perish as temperatures decline. Contrary to the findings of Chapter 1, in Chapter 2, I use thousands of RADseq loci and show strong support that temperate inhabitants are genetically diverged from southern populations and are composed of an isolated and persistent ancestral gene pool. The aim of Chapter 3 is to investigate how major current forces as well as climatic and geographic processes have shaped the evolutionary and demographic history of western Atlantic seahorses (Hippocampus) and pipefishes (Syngnathus). This Chapter takes a comparative approach across five codistributed species (two seahorses and three pipefishes). Genomic patterns of subpopulation divergence and post-divergence gene flow may be shared amongst fish species with similar life history traits, however ecological differences (i.e., macroclimatic tolerance and rafting propensity) may impact the rates of gene exchange and/or isolation times between subpopulations. The result of this study show how directional ocean currents and the life history trait of rafting propensity impacts population divergence and connectivity, and predicts gene flow directionality and magnitude in four out of five of the focal taxa. Lastly in Chapter 4, I use a molecular forensics approach to track the U.S. dried seahorse trade. Due to global exploitation, the genus Hippocampus are the only fish to have all species listed under the Convention of International trade of endangered species (CITES). Millions of individuals are traded each year for the use in traditional Chinese medicine as well as for souvenirs and crafts. Using DNA barcoding, while mentoring high school and undergraduate students, we identified and compared specimens collected from two primary U.S. dried seahorse end-markets: 1) traditional Chinese medicine and, 2) Internet and coastal souvenir retailers. The results of this study found a significant contrast in both the species composition and size of individuals being sold between each market

Investigation of mobile devices usage and mobile augmented reality applications among older people

Mobile devices such as tablets and smartphones have allow users to communicate, entertainment, access information and perform productivity. However, older people are having issues to utilise mobile devices that may affect their quality of life and wellbeing. There are some potentials of mobile Augmented Reality (AR) applications to increase older users mobile usage by enhancing their experience and learning. The study aims to investigate mobile devices potential barriers and influence factors in using mobile devices. It also seeks to understand older people issues in using AR applications