Towards Edge-Based Data Lake Architecture for Intelligent Transportation System

TL;DR

This paper proposes an edge-based data lake architecture tailored for intelligent transportation systems, aiming to efficiently handle complex, large-scale data to improve decision-making and service innovation.

Contribution

It introduces a scalable, fault-tolerant edge-based data lake architecture specifically designed for ITS data processing and analysis.

Findings

Improved data processing efficiency for ITS applications

Enhanced decision-making capabilities in transportation systems

Validated effectiveness through three diverse use cases

Abstract

The rapid urbanization growth has underscored the need for innovative solutions to enhance transportation efficiency and safety. Intelligent Transportation Systems (ITS) have emerged as a promising solution in this context. However, analyzing and processing the massive and intricate data generated by ITS presents significant challenges for traditional data processing systems. This work proposes an Edge-based Data Lake Architecture to integrate and analyze the complex data from ITS efficiently. The architecture offers scalability, fault tolerance, and performance, improving decision-making and enhancing innovative services for a more intelligent transportation ecosystem. We demonstrate the effectiveness of the architecture through an analysis of three different use cases: (i) Vehicular Sensor Network, (ii) Mobile Network, and (iii) Driver Identification applications.