Data Sharing at the Edge of the Network: A Disturbance Resilient Multi-modal ITS

TL;DR

This paper explores how edge computing can enable resilient data sharing among multimodal transport systems in MaaS, addressing challenges like latency, bandwidth, and privacy during disturbances.

Contribution

It introduces a framework for disturbance-resilient data sharing in M-ITS using edge computing to enhance system robustness and responsiveness.

Findings

Edge computing reduces latency in data sharing.

Data sharing improves system resilience during disturbances.

Bandwidth and storage constraints are critical considerations.

Abstract

Mobility-as-a-Service (MaaS) is a paradigm that encourages the shift from private cars to more sustainable alternative mobility services. MaaS provides services that enhances and enables multiple modes of transport to operate seamlessly and bringing Multimodal Intelligent Transport Systems (M-ITS) closer to reality. This requires sharing and integration of data collected from multiple sources including modes of transports, sensors, and end-users' devices to allow a seamless and integrated services especially during unprecedented disturbances. This paper discusses the interactions among transportation modes, networks, potential disturbance scenarios, and adaptation strategies to mitigate their impact on MaaS. We particularly discuss the need to share data between the modes of transport and relevant entities that are at the vicinity of each other, taking advantage of edge computing technology to avoid any latency due to communication to the cloud and privacy concerns. However, when sharing at the edge, bandwidth, storage, and computational limitations must be considered.