Design with Uncertainty: The Role of Future Options for Infrastructure Integration

Martani, Claudio; Jin, Ying; Soga, K; Scholtes, Stefan

doi:10.1111/mice.12214

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…The air pollution has been becoming more and more frequent and severe in China, and is increasingly considered as a major factor to affect a city's livability and residents' well-being. Indeed, the greenhouse gas (GHG) emissions have attracted much attention by government agencies, academia, as well as the general public (Kolmanovsky et al, 2011;Liao et al, 2012;Slavin et al, 2013;Yin et al, 2015;Martani et al, 2016;Jung et al, 2016;Levin et al, 2016;Zockaie et al, 2016;Taheri et al, 2016;Chen and Guan, 2017). Almost 30% of GHG emissions are determined by the amount of fuels consumed by the transportation sector, which has been a major contributor to energy consumption and C 2018 Computer-Aided Civil and Infrastructure Engineering.…”

Section: Introductionmentioning

confidence: 99%

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Wei

Easa

et al. 2018

Computer aided Civil Eng

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In this research, by taking advantage of dynamic fuel consumption–speed data from Internet of Vehicles, we develop two novel computational approaches to more accurately estimate truck fuel consumption. The first approach is on the basis of a novel index, named energy consumption index, which is to explicitly reflect the dynamic relationship between truck fuel consumption and truck drivers’ driving behaviors obtained from Internet of Vehicles. The second approach is based on a Generalized Regression Neural Network model to implicitly establish the same relationship. We further compare the two proposed models with three well‐recognized existing models: vehicle specific power (VSP) model, Virginia Tech microscopic (VT‐Micro) model, and Comprehensive Modal Emission Model (CMEM). According to our validations at both microscopic and macroscopic levels, the two proposed models have stronger performed in predicting fuel consumption in new routes. The models can be used to design more energy‐efficient driving behaviors in the soon‐to‐come era of connected and automated vehicles.

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Section: Introductionmentioning

confidence: 99%

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Wei

Easa

et al. 2018

Computer aided Civil Eng

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“…Several studies have proposed approaches to assess the reliability, resilience, vulnerability, and failure process of power, transportation, and water supply networks individually (Barabási & Albert, 1999; Buldyrev, Parshani, Paul, Stanley, & Havlin, 2010; Jenelius & Mattsson, 2012; Ouyang & Fang, 2017; Pietrucha‐Urbanik & Tchórzewska‐Cieślak, 2018; Sumalee & Kurauchi, 2006; Ulak, Kocatepe, Sriram, Ozguven, & Arghandeh, 2018). However, these critical infrastructures are interconnected and interdependent (Alinizzi, Chen, Labi, & Kandil, 2018; Homeland Security, 2019; Martani, Jin, Soga, & Scholtes, 2016). Considering the increased connectedness and interdependencies among infrastructure systems, studies have proposed a holistic approach to assess the resilience to disruptions (Hasan & Foliente, 2015; Lu et al., 2018; Pant, Thacker, Hall, Alderson, & Barr, 2018; Rinaldi, Peerenboom, & Kelly, 2001; Sriram et al., 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Roy

Hasan

Mozumder

2020

Computer aided Civil Eng

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Rapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the cooccurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption-whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo-parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption-related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co-occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations. 1 INTRODUCTION Cities and communities all over the world largely depend on critical infrastructure systems/services such as electrical power, water distribution, communication services, and transportation networks. The growing interconnectedness and © 2020 Computer-Aided Civil and Infrastructure Engineering interdependency among these systems have changed the organizational and operational factors and increased the vulnerability in the face of unwanted disruptions. These systems provide critical services to a large population, and thus when disrupted they affect our quality of life, local and regional economy, and the overall community wellbeing. The need to

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“…In the example in this paper though, it is assumed that levels of service are constant. More information on investigating changes in levels of services can be found in the publications by Martani et al (2016Martani et al ( , 2018, Esders et al (2015Esders et al ( , 2016, De Neufville and…”

Section: How Objects Change Over Timementioning

confidence: 99%

Estimating and communicating the risk of neglecting maintenance

Adey

Martani

Papathanasiou

et al. 2019

Infrastructure Asset Management

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The difficulties of engineers and managers agreeing on how to invest in infrastructure maintenance stem from a basic inability to communicate with each other. This leads to the suboptimal management of infrastructure. Luckily, this situation can be remedied by engineers learning how to communicate their concerns in a way that managers can understand, so that they can clearly see whether a proposed action needs to be taken or can be deferred. This paper shows, with the help of a realistic example, in terms of infrastructure, methodology and techniques, how this can be done. The proposed approach, although upon reading is perhaps intuitive, is starkly absent in the literature in the field of infrastructure asset management. In the proposed approach, it is demonstrated how to improve the traditional approaches used by engineers to communicate to managers through (a) the quantification of the level of service as seen by mangers, (b) the modelling of how infrastructure might not provide the required level of service and (c) the way of showing how intervention programmes can affect the provision of service, both now and in the future.

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Design with Uncertainty: The Role of Future Options for Infrastructure Integration

Cited by 11 publications

References 31 publications

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Estimating and communicating the risk of neglecting maintenance

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