The disruption transport model: computing user delays resulting from infrastructure failures for multi-modal passenger & freight traffic

Marieke S Van Der Tuin,Adam J Pel

doi:10.1186/s12544-020-0398-9

Marieke S Van Der Tuin, Adam J Pel

Open Access

https://doi.org/10.1186/s12544-020-0398-9

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Abstract

Transport infrastructure owners are moving from reactive toward proactive infrastructure management. This involves computation of costs associated with failure or maintenance, including expected transport delays. These delays are often computed by multiplying additional travel time by the number of travellers. However, this does not reflect the process of decision-making by travellers using the infrastructure asset, such as mode choices, departure time changes and trip cancellations to reduce time wasted in a traffic jam. Therefore, we introduce a multi-modal transport model that simulates travellers’ behaviour after a large-scale infrastructure failure at a critical node in the European TEN-T network. We use a novel approach of modelling the region around the infrastructure disruption in a very detailed manner, whereas the rest of Europe is modelled in a more basic way. This enables us to model impacts of disruptions in high detail, whereas also effects throughout Europe are considered, within reasonable computation time.

Highlights

In the recent years, the number of infrastructure failures has increased due to ageing of infrastructure, more extreme weather events due to climate change, and increased traffic loading
2.4 Conclusion Following the review on behaviour after infrastructure failures, it can be concluded that four types of behaviour are shown regularly and should be included in a transport model simulating disruption: route changes, departure time choices, mode shifts and trip cancellations
The total user delays associated with the disruption are calculated as the sum of those predicted by the Local Disruption model (LD) model and the Global Spillover model (GS) model

Summary

Introduction

The number of infrastructure failures has increased due to ageing of infrastructure, more extreme weather events due to climate change, and increased traffic loading. The number of maintenance projects and resulting (partial) closures of the transport network has increased. This leads to a growing interest for making robust, cost-effective decisions on maintaining and upgrading infrastructure to prevent failures. Transport infrastructure owners are moving from reactive infrastructure management toward proactive management, by prioritising upgrades for critical network assets. Nowadays, such predictive maintenance techniques incorporate measuring the condition of assets, combined with life cycle costs analysis (LCC). The LCC usually computes these user costs (e.g. loss of service due to congestion and detour) by multiplying the (assumed) increased travel time by the number of travellers using the link [13]

Methods

Findings

Conclusion