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21st Century Operations Using 21st Century Technologies

Scoping and Conducting Data-Driven 21st Century Transportation System Analyses

Executive Summary

The 21st Century analyst can readily draw upon improvements in commercially supported analytic software, increasingly powerful advanced computational platforms, and a data-rich environment to model large and dynamic surface transportation systems. However, have the gains made by the individual analyst outstripped the gains made by the organizations that manage transportation systems to capitalize on new analytic techniques? When analysts work in relative isolation from the mission of improving surface transportation system performance, they are frequently not involved in diagnosing transportation system problems or using data to help scope an analytic project. Data and models developed for past projects are discarded, lost, or documented so poorly that they cannot be leveraged for future projects. Too often, there is a lack of advanced institutional models that systematically and consistently leverage the power of transportation analytics embedded within transportation system management's broader mission.

The Federal Highway Administration (FHWA) has developed guidance on how 21st Century transportation analytic resources (data, tools, and computational platforms) can be systematically embedded within the transportation system management process, with four significant results:

  • Enhanced characterization of transportation system dynamics and problem diagnosis.
  • Improved analytic project scoping.
  • Data-driven experimental design that limits risk and maximizes analytic insight.
  • Systematic execution and documentation of analyses to preserve accrued analytical capital.

The FHWA guidance supports transportation professionals—including data managers and transportation analysts—at different levels of technical expertise and in a wide variety of uses across short-, medium- and long-term decision horizons. As shown in Figure 1 (on the next page), the 21st Century Analytic Project Scoping Process consists of a four-module Continuous Improvement Process (CIP):

  • Module 1: System Diagnostics. Characterize system dynamics and diagnose problems.
  • Module 2: Scoping. Perform data-driven transportation analysis project scoping.
  • Module 3: Preparing Data. Collect and organize the data needed to conduct a transportation analysis.
  • Module 4: Analysis. Conduct and document transportation analyses.

In Module 1, the analyst uses system performance measures and early diagnostic activities to develop preliminary analytic problem statements and prioritize the identified problem statements with a risk-reward project-screening approach. The Analytical Problem Statement(s) associated with a high-priority concept—the final product of this module—is the foundational connecting document that initiates the second major step in the process.

The project scoping in Module 2 includes a more detailed project definition, the identification of project-specific performance measures, a refinement of mitigation strategies and data needs, tool selection, and cost and schedule estimation. This guidance includes a scoping tool that helps the analyst complete the cost and schedule estimation. Module 2 ends when the Project Scoping Summary is completed. The summary provides enough information to initiate data preparation and analysis work (Module 3).

Figure 1. Diagram. 21st Century analytic project scoping process.

Figure 1 shows the 4 module continuous improvement process. The 4 modules are part of a continuous cycle, with Module 1 leading to Module 2, Module 2 leading to Module 3, Module 3 leading to Module 4, and Module 4 leading back to Module 1. Insights from all 4 modules contribute to an organization's analytical capital (data and models).

(Source: Federal Highway Administration.)

In Module 3, a data analyst verifies the consistency and quality of the available data and outlines the data collection plan to fill up the gap between the data needs and availability. Depending on the nature and scope of the project, the data analyst may identify and summarize a representative set of operational conditions that are critical for creating a strong analytical plan (Module 4).

To execute an analytical project (Module 4), the analyst creates a detailed design with experimental and control cases, as well as calibration and validation of models under various operational conditions and a sensitivity analysis. Documenting the results is the final step—the analyst documents project findings to inform decision-making and also captures lessons learned to improve the agencies' implementation of the 21st Century analytic project scoping process (diagnostics, scoping, data preparation, and analytics) for the next cycle.

Following this four-step process enables transportation system management organizations to successfully achieve their objectives and realize a number of important positive outcomes related to greater insight, better analyses, and reduced costs and risks:

  • Increased understanding of how the system as a whole operates and changes over time.
  • More relevant and targeted transportation analytics.
  • Minimized redundancy and technical risk in a portfolio of analytical projects.
  • Reduced costs of conducting analyses over time.
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