Future-Proof Transportation Infrastructure Through Proactive, Intelligent, and Public-Involved Planning and Management

Details

• Creators:
  Chowdhury, Sudipta ; Zhu, Jin
• Corporate Creators:
  University of Maine. Transportation Infrastructure Durability Center (TIDC) ; University of Connecticut. Department of Civil and Environmental Engineering
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Data Collection Decision Making Environmental Impacts Financing Infrastructure Innovation Literature Reviews Management Pavements Performance Planning Quantitative Analysis Structural Design

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• Publication/ Report Number:
  69A3551847101
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Abstract:
  Transportation infrastructure planning is a complex process, which requires the consideration of a multitude of factors. While many existing studies have investigated the impacts of different factors individually, a holistic framework that encompasses a comprehensive list of influencing factors on transportation infrastructure planning and their inter-relationships is still missing. Especially, many forward-looking factors are often overlooked in current planning frameworks. To this end, this study aims to develop a future-proofed transportation infrastructure planning framework with a focus on roadways, bridges, and transit. Three parts of work are included in this study: (1) First, a list of important and emerging factors that affect or may affect transportation infrastructures was identified from 48 published technical reports and journal articles on future-proofed transportation infrastructure planning via two topic modelling techniques: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF). These factors were later compiled and converted to a four-level taxonomy via bottom-up grouping. For example, public transportation programs and public-private partnerships were grouped together as the two primary sources of funding for transportation infrastructures. (2) Second, association rule mining (ARM) was then used to discover relationships among these factors. Specifically, two quantitative association rule mining metrics: confidence (frequency of association) and lift (strength of association were used. In total, 102 inter-relationships were identified, among which eight interrelationships were found to be significant. For example, a significant association was found between societal trends with environmental performance. It implies that in order to achieve a better environmental performance of transportation infrastructure, capturing and taking advantage of societal trends could be useful, since societal trends such as less dependency on personal vehicles can significantly reduce the environmental impact of transportation infrastructures (e.g., less emission). (3) Finally, based on the significant association rules identified between new technology and other factors, two case studies were conducted to quantify the impacts of the electric vehicle as an example of new technologies on different aspects of transportation infrastructure. Quantitative scenario analysis was performed to facilitate informed decision-making under uncertainty. This framework has the potential to turn into a smart decision-making system that can help transportation infrastructure owners, designers, builders, governments, and operators to have a holistic approach to plan, build, and manage our transportation infrastructures in the face of future risks and uncertainties.
• Format:
  PDF
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:bd188cfce1a0963fbdbb945c40e40731013717bb461700ce3bc881f9440787473288c8eb4e7a6d9cfdde769a865e6dfa0cd17caae5a9d1cc331dde19c2691dee
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88943/dot_88943_DS1.pdf
• File Type:
  [PDF - 2.43 MB ]