Using Information at Different Spatial Scales to Estimate Demand to Support Asset Management Decision Making

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CAIT project no.: CAIT-UTC-NC6

Fiscal Year: 2014/2015

Status: Final

Rutgers-CAIT Author(s): Sue McNeil, Ph.D., P.E., University of Delaware, Patrick Szary, Ph.D., CAIT

External Author(s): Frank Lawrence, Sea Bright, Dina Long, Sea Bright

Sponsor(s): USDOT, Township of Sea Bright


This project builds on two ongoing CAIT projects: the UD project “Understanding the Relationships between Household Decisions and Infrastructure Investment in Disaster Recovery: Cases from Superstorm Sandy” and the collaborative project (involving UD, Rutgers, and Utah) “Big Data: Opportunities and Challenges in Asset Management.” These projects have identified some important large data sources, including survey and sensor data,  that are relevant to forecasting demand and understand the needs of communities. In addition other parallel efforts provide map based data on infrastructure vulnerability (for example, “Climate Change Vulnerability and Risk Assessment of NJ’s Transportation Infrastructure”). Furthermore, the project is consistent with the MAP-21 requirement for states to develop risk-based asset management programs.

The research will begin with a literature review to build on relevant new research and initiate an inventory of relevant data and methods. The research team will then develop a framework for integrating the data to support asset management functions. This is more comprehensive in terms of the types of data than the research currently being conducted as part of the UD project and more focused on demand estimation than the exploratory research that is part of the “big data” collaborative project. Using the framework, a case study focused on Sea Bright, NJ, will be developed. Our clients are the mayor of Sea Bright, and chair of the Sea Bright 2020 Steering Committee but we expect to engage other participants. We will draw on input from the community in the form of the survey we are about to launch, reports from community meetings, past plans and studies (for example a Smart Growth plan and a study by Bloustein School of Planning and Public Policy students), and plans, GIS, and transportation data from Monmouth County, North Jersey Transportation Planning Authority, and New Jersey DOT. The case study will be presented at a workshop and the framework updated to reflect comments and other inputs.