Stochastic event simulation model for quantitative prediction of road tunnel downtime

https://doi.org/10.1016/j.tust.2021.104092Get rights and content

Highlights

  • •

    Events impacting tunnel functionality are identified based on duration & intensity.

  • •

    Stochastic simulation model built to evaluate the downtime of tunnel infrastructure.

  • •

    Separate modeling mechanism for event types.

  • •

    Functionality loss quantified using a numeric metric.

  • •

    Metric is used to compute resilience of road tunnels to disruptive events generated from the model.

Abstract

Given the importance of road tunnels in a transportation network, it is essential to quantitatively assess and predict tunnel functionality loss due to disruptive events. In this study, a stochastic event simulation model was developed to evaluate the resilience of tunnel infrastructure, quantified using a functionality metric as a function of loss in traffic capacity and its duration. The model consists of individual modules to account for disruptive events that cause tunnel closures. In this paper, the mechanism and probabilistic models used for each simulation module are presented. A simulation for Eisenhower tunnel, Colorado, was conducted using the proposed method as a validation case study. The results showed that the proposed model could simulate realistic tunnel operation status. While the validation was made for a specific tunnel, the model was designed to be applied to the resilience analysis of any road tunnel with defined design and operation parameters.

Section snippets

Introduction and background

Road tunnels are an essential part of modern transportation infrastructure. The construction of tunnel infrastructure is relatively expensive and time-consuming as compared to other transportation infrastructure. However, once completed, a tunnel is generally an efficient solution to transportation needs spatially and environmentally. With the increasing urbanization and environmental considerations, society and the transportation network are expected to become increasingly dependent on tunnel

Simulation methodology

This study aims to develop a Monte-Carlo-type probabilistic simulation model that will generate functionality loss events and corresponding tunnel closure duration. Firstly, a numerical metric for tunnel functionality is defined, which was adopted from a previous study (Khetwal et al., 2019) as:Q(t)=#ofopenlanes(Ln(t))Total#oflanesLtot×Reducedspeedlimit(Sn(t))Normalspeedlimit(S)where Ln(t) is the number of lanes that are open to traffic at time t, Ltot is the total number of lanes, Sn(t) is the

Model implementation

The probabilistic model presented in the previous section was implemented using the Monte Carlo simulation. In this study, a five-module program was developed in MATLAB to simulate tunnel functionality time history considering random and planned disruptive events. The overall structure of the program is shown in Fig. 5. Each of the five modules is responsible for generating the occurrence and tunnel functionality loss corresponding to a specific type of event discussed in the previous section.

Simulation example and preliminary validation

While a probabilistic simulation model cannot be validated on an individual event basis, model outcome validation can be performed by comparing the simulated random event distribution with realistic tunnel operation data and observing the trend on event distribution. Because tunnel operation resilience has not been studied widely, the amount of data suitable for overall operation simulation validation is minimal. In a previous study (Khetwal et al., 2020b), realistic tunnel functionality data

Conclusion

To evaluate tunnel functionality loss and resilience quantitatively, a tunnel functionality metric was defined by its ability to accommodate through traffic. In this study, a probabilistic simulation framework was established to simulate tunnel functionality over time by considering several critical influential factors and random disruptive events. Within the proposed framework, the occurrence and severity of disruptive events were simulated using underline probabilistic models (model

CRediT authorship contribution statement

Sandeep Singh Khetwal: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing. Shiling Pei: Conceptualization, Writing – original draft, Writing – review & editing. Marte Gutierrez: Writing – review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The authors gratefully acknowledge the financial support of the University Transportation Center for Underground Transportation Infrastructure (UTC-UTI) at the Colorado School of Mines under Grant No. 69A3551747118 from the US Department of Transportation (DOT). The opinions expressed in this paper are those of the authors and not of US DOT. The authors would also like to acknowledge the assistance of Colorado Department of Transportation (especially Weiyan Chen and Steve Harrelson) for their

References (34)

  • S. Engelhardt et al.

    Recommendations for the Determination of Lifecycle Costs for Road Tunnels

    (2019)
  • Fatality and Injury Reporting System Tool (FIRST), version 2.0.1 (2020) (https://cdan.dot.gov/query). Accessed March...
  • FHWA (Federal Highway Administration), 2015. Specifications for the national tunnel inventory (SNTI). (Publication no....
  • FHWA (Federal Highway Administration), 2015. Tunnel operations, maintenance, inspection, and evaluation (TOMIE) manual....
  • A. Henke et al.

    The 2001 Gotthard Road Tunnel Fire

    Tunn. Manage. Int.

    (2004)
  • Highway Statistics Series. Federal Highway Administration, 2019. Retrieved from...
  • Highway Vehicle Fires (2014–2016), 2018. Topical Fire Report Series, 19(2),...

    • Cited by (5)

    View full text
    © 2021 Elsevier Ltd. All rights reserved.