An Adaptive System Architecture for Multimodal Intelligent Transportation Systems

TL;DR

This paper proposes an adaptive, layered architecture for multimodal intelligent transportation systems, enhancing scalability, data integration, and stakeholder collaboration to improve efficiency and user experience.

Contribution

It introduces a novel adaptive, user-centric system architecture that integrates diverse data sources and supports scalable, multi-stakeholder transportation services.

Findings

Architecture supports seamless stakeholder interactions

Data integration enhances decision-making

Use cases demonstrate system effectiveness

Abstract

Multimodal intelligent transportation systems (M-ITS) encompass a range of transportation services that utilise various modes of transport and incorporate intelligent technologies for enhanced efficiency and user experience. There are several challenges in M-ITS including data integration, Interoperability, scalability, user experience, etc. To address these challenges, such a system requires an adaptive system architecture that enables M-ITS to operate as an integrated ecosystem. In this paper, we provide an adaptive, user-centric, and layered architecture for multimodal transportation systems. The proposed architecture ensures scalability for seamless interactions of various subcomponents, that are often managed by different stakeholders. Concurrently, the data architecture is detailed, covering diverse data sources, advanced analytics, and stringent governance, providing a robust basis for intelligent decision-making. We provide two example use cases of the proposed architecture, showing how the data architecture and the system architecture can be fused and serve multimodal intelligent transport services.