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

Igor Mikolasek, Saeedeh Ghanadbashi, Nima Afraz, Fatemeh Golpayegani

TL;DR

The paper addresses resilience of Mobility‑as‑a‑Service (MaaS) in disturbances by formalizing a Multimodal ITS (M‑ITS) framework that uses edge computing to decentralize data processing, reduce latency, and preserve privacy. It develops a disturbance‑mode‑network model to characterize how disruptions affect different transport modes and networks, and prescribes selective, edge‑driven data sharing and warning dissemination to relevant actors. The work outlines practical detection options and adaptation strategies for disturbances such as accidents, work zones, infrastructure failures, and rescue events, with data sources including Waze, C‑ITS/V2I, and TF data. The findings offer a concrete path toward resilient, sustainable urban mobility with near‑real‑time, multimodal coordination and targeted information distribution that minimizes information overload while maintaining service continuity.

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.

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

TL;DR

The paper addresses resilience of Mobility‑as‑a‑Service (MaaS) in disturbances by formalizing a Multimodal ITS (M‑ITS) framework that uses edge computing to decentralize data processing, reduce latency, and preserve privacy. It develops a disturbance‑mode‑network model to characterize how disruptions affect different transport modes and networks, and prescribes selective, edge‑driven data sharing and warning dissemination to relevant actors. The work outlines practical detection options and adaptation strategies for disturbances such as accidents, work zones, infrastructure failures, and rescue events, with data sources including Waze, C‑ITS/V2I, and TF data. The findings offer a concrete path toward resilient, sustainable urban mobility with near‑real‑time, multimodal coordination and targeted information distribution that minimizes information overload while maintaining service continuity.

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.
Paper Structure (6 sections, 2 figures, 1 table)

This paper contains 6 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Transportation Modes and Networks
  3. Service Disturbances to Mode-Network Combinations and M-ITS Adaptation Strategies
  4. Data Sharing at the Network Edge
  5. Conclusions
  6. Acknowledgements

Figures (2)

  • Figure 1: Overview of fundamental transportation modes and networks employed. The indices $M_i$ and $N_i$ are employed for mode and network respectively and specifying the pairings between the two in Fig. \ref{['fig:effects of disturbances on different combinations']}.
  • Figure 2: Effects of different sources of disturbance on different combinations of modes and networks.