WITHDRAWN: Design of visual art elements in a sustainable urban transportation system information platform

Abstract

Many cities and countries strive to create smart transportation systems that use the abundance of multisource and multi-data on transport infrastructure functionality and improve human mobility, interests, and lifestyle. The challenges in a sustainable urban transportation system include behavioral visualization, road behavior classification, anomalies detection, and traffic prediction by the human operator. In this paper, an interactive visual analytics localization mapping framework (IVALMF) has been proposed to enhance the exploration by unified interactive interfaces of historical data and prediction of future traffic. Furthermore, low-cost traffic data analysis is introduced to enhance visual analytics related to analyzing motion and transport systems. Hypothetical information clustering analysis is integrated with IVALMF to enable the exploration, visual detection of rare events, testing hypotheses, and prevention of traffic flow supported by advanced data analytical algorithms of the behavioral similarities among roads. The simulation analysis is performed based on scalability and efficiency, proving the reliability of the proposed framework with 96.17%. • An interactive visual analytics localization mapping framework (IVALMF) has been proposed to enhance the exploitation of future traffic.