Multimodal System Performance Measures Research and Application: Innovation and Research Plan
Chapter 4. Innovation Plan
The study team understood at the outset of the research that measuring multimodal system performance required a fundamental change in perspective and a new set of data, and that developing such measures would occur in phases. This research represents the first phase of the overall effort, with the goal of gaining insights that could add specificity to needed research. The innovation plan presented in this chapter outlines three potential innovation initiatives, which in turn helped define potential topics for further research included in the next chapter.
Develop and Obtain Multimodal System Productivity Data
As highlighted in earlier chapters, an integrated multimodal system performance measure requires complete person trip data. Conversations with data providers found evidence that this information is collected by companies such as Google and Apple but is not available for transportation planning because of privacy and business model issues. The first proposed initiative of the innovation plan is obtaining and/or developing accurate continuous and traced complete person trip data.
This initiative would explore options, such as:
- Reaching an agreement with Google, Apple, or other companies to provide the data. Such an agreement would address privacy issues, costs, access, etc.
- Working with vendors such as StreetLight, Streetlytics, and Mobility Labs to improve methods for expanding smaller sample sizes available from other companies. All three companies are using similar methods for expanding data and exploring new ways to improve these methods. For example, Google is comparing expanded data developed by Mobility Labs, an Alphabet company, to check the accuracy of these data.
- Working independently to identify methods for improving crowd source data. Such methods could include calibrating crowd-source data using travel data or developing apps which collect travel survey data.
- Work with other agencies and private companies to improve the information used to expand complete trip samples. For example, vendors use U.S. Census Bureau demographic data, and adjustments to the kinds of information that are collected could improve the expansion methods.
Differing mechanisms can be used to test the differing strategies, such as:
- Awarding demonstration grants (such as the Smart City or Advanced Transportation and Congestion Management Technology Deployment – ATCMTD - grants) to agencies and companies interested in testing strategies. Differing grants could explore each of the strategies listed above or others, allowing for comparisons that ultimately define the preferred strategy.
- Coordinate with stakeholder groups such as Transportation Research Board (TRB), the American Association of State Highway and Transportation Officials (AASHTO), and the American Public Transit Association (APTA) to develop and fund research on the strategies.
- Incorporating this initiative into existing performance management initiatives, such the Transportation Performance Management effort sponsored by the FHWA and the Transportation Performance hub and portals sponsored by AASHTO.
Refine the "Ideal" Multimodal System Productivity Measures(s)
The second initiative of the innovation plan proposes to build on the concepts presented in this research and refine the MSP into a practical, implementable, and accepted measure. The types of refinements suggested include:
- Further developing the complete trip perspective framework. As noted in earlier chapters, a complete trip perspective and the concept of productivity underpin multimodal system performance measurement. This initial multimodal measure research effort may only touch on foundational concepts; more research may be needed to further define and understand the perspective, like the research undertaken over time to develop the Highway Capacity Manual.
- Defining the MSP measure. As noted earlier, the MSP can be estimated in differing ways. While estimation is not the ultimate objective (actual calculated performance is), it is a first step in better understanding the MSP measure. This step in the refinement process determines the optimal way(s) for calculating the MSP given the characteristics of available data.
- Testing MSP applications. This report identifies how the MSP can be used to provide feedback on productivity, efficiency, and resiliency. Detailed tests of the measure across those and other areas will refine and illustrate how the MSP can be used for planning, programming and management. and operations.
The mechanisms available to accomplish this effort are similar to those listed under the first initiative, including demonstration grants, funded research, and alignment with current performance management efforts.
