PROJECT TITLE :

Visual Analysis of Multiple Route Choices based on General GPS Trajectories - 2017

ABSTRACT:

There are often multiple routes between regions. Drivers select completely different routes with completely different issues. Such issues, have continually been a purpose of interest in the transportation space. Studies of route selection behaviour are sometimes primarily based on small vary experiments with a cluster of volunteers. However, the experiment information is kind of restricted in its spatial and temporal scale plus the practical reliability. In this work, we tend to explore the chance of studying route alternative behaviour based on general trajectory dataset, that is a lot of realistic in an exceedingly wider scale. We tend to develop a visual analytic system to help users handle the large-scale trajectory knowledge, compare totally different route selections, and explore the underlying reasons. Specifically, the system consists of: 1. the interactive trajectory filtering that supports graphical trajectory query; two. the spatial visualization that provides an outline of all feasible routes extracted from filtered trajectories; 3. the factor visual analytics which provides the exploration and hypothesis construction of various factors' impact on route choice behaviour, and therefore the verification with an integrated route selection model. Applying to real taxi GPS dataset, we have a tendency to report the system's performance and demonstrate its effectiveness with 3 cases.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Iterative Refinement for Multi-source Visual Domain Adaptation ABSTRACT: One of the most difficult aspects of multi-source domain adaptation is figuring out how to minimize the differences in domains that exist
PROJECT TITLE : Learning Versatile Convolution Filters for Efficient Visual Recognition ABSTRACT: This article presents versatile filters that can be used to construct efficient convolutional neural networks, which are widely
PROJECT TITLE : Deep Visual Odometry with Adaptive Memory ABSTRACT: A novel deep visual odometry (VO) method that takes into account global information by selecting memory and refining poses is presented here. The currently available
PROJECT TITLE : Iterative Refinement for Multi-source Visual Domain Adaptation ABSTRACT: One of the most difficult aspects of multi-source domain adaptation is figuring out how to minimize the differences in domains that exist
PROJECT TITLE : Attention in Reasoning Dataset, Analysis, and Modeling ABSTRACT: Although attention has become an increasingly popular component in deep neural networks for the purpose of both interpreting data and improving

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry