Citation

Ammar A, Maier F, Catchings R, Nassereddine H, Dadi G. Transp. Res. Rec. 2023; 2677(11): 428-445.

Copyright

(Copyright © 2023, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981231166000

PMID

unavailable

Abstract

The mission of state Departments of Transportation (DOTs) has evolved, and their perception has shifted from focusing on constructing new assets to managing and maintaining existing transportation assets and optimizing asset performance. In response to this change, state DOTs began adopting a "transportation asset management" approach, an intensive data-driven decision-making process to maintain and extend the serviceability of transportation assets throughout their lifecycle. However, state DOTs continue to face challenges in conducting cross-asset system analysis and integrating data across systems throughout the asset lifecycle. Conversely, emerging technologies, namely Digital Twins, have the potential to leverage the value of asset data and transform data into valuable insights to inform decision-making. The definition of Digital Twins in the infrastructure industry is inconsistent, and the transition toward a digitized built environment--a preliminary step required for a successful implementation of Digital Twins--has not been investigated. Thus, this paper presents the burning platform for the need for Digital Twins and defines the concept. Additionally, this study investigates the current practices of state DOTs toward the digital transition of their transportation asset data and contextualizes the DOTs' maturity in the advancement of digital processes. This paper focuses on ancillary asset systems, particularly Roadside, Electronic, and Drainage Asset systems. A web-based survey was developed and distributed to state DOTs for data collection. For each asset, the perceived timeline for digitization was investigated, and three variables related to data digitization were explored: data format, data level of detail, and data collection technique.

Language: en