Multimodal System Performance Measures Research and Application: Innovation and Research Plan
Chapter 5. Research Program and Projects
The innovation plan presented in the last chapter lays out the proposed innovation initiatives for multimodal system performance measurement. This chapter suggests topics for future research needed to further develop and implement the MSP.
Innovation Plan Support
The following sections list related research to support the innovation plan initiatives. Within each of the sections are recommendations for specific research projects.
Data Development Research
Research to Improve the Accuracy and Calibration of Data. Multimodal system measurement requires detailed network data and continuous, traceable, complete trip data. Detailed network data are readily available apart from bicycle and pedestrian facility data. Those data typically are updated continually by public agencies and private companies to account for errors and network changes. This research program focuses on methods for collecting and calibrating complete trip data developed from privately owned mobility devices and publicly owned detection devices.
- Research to Further Develop and Apply Mobility Device Data. Crowdsourced mobility data record time and location stamps for individual trips, resulting in traced complete person trip information needed by the MSP. Research will identify and develop methods for expanding and calibrating data, building on the work of companies such as StreetLight, Streetlytics, and Mobility Labs. It will also identify ways to download, manipulate, review, and store large datasets.
- Research to Further Develop and Apply Travel Data. Travel data record the number of trips counts and, in some cases, travel speeds, at recording locations to provide a means of enhancing and calibrating mobility data. Research will identify methods for calibrating mobility device data using travel information, including methods for optimally locating detection data collection devices for calibration. It will also identify ways to download, manipulate, review, and store data.
Research to Improve the Coordination of Multimodal Data Collection. Current travel data collection efforts reflect the modal orientation of planning and performance monitoring, resulting in a lack of complete, overlapping information across modes. The national scan for pilot sites conducted under this research confirmed this problem. This research also found that privately developed crowdsourced data samples are expanded with several data sources, including travel data, but those companies indicated calibration would improve with better data. A multimodal system performance measure will need a more coordinated, systematic data collection approach. The purpose of this research is to develop cost effective methods for coordinating the collection, sharing, and storage of network, crowd-source, and travel data.
Research into the Acquisition of Data. Crowdsourced data from private companies is not free. Increasingly, transportation agencies are purchasing those data for use across multiple agencies. For example, the Virginia Department of Transportation purchased StreetLight data for use by regional and local agencies across the Commonwealth. Research will identify innovative methods for acquiring data and negotiating cost effective data purchase agreements with vendors.
Measure Development Research
This Innovation and Research Plan focuses on the steps to implement multimodal system performance measures and acquire the data needed for such measures. It is but a first step in implementing an "ideal" measure. In the meantime, research in this area will focus on how to reframe traditional transportation performance measurement from a single mode, facility-based perspective to a multimodal complete trip-based perspective. The key areas of measure development research are:
Research into the Geography and Perspective of Multimodal System Performance Measurement. As noted in this report, multimodal system performance measurement introduces both scalability and perspective opportunities and challenges. Defining the system to measure, particularly at the corridor and sub-area levels, presents the primary challenge that could be simplified with complete trip data and a place-based perspective. Such changes will create a new analysis framework for most transportation agencies. Research will identify and develop methods for measuring multimodal system performance at differing geographic scales and from both perspectives.
Research into Measuring Multimodal System Performance Across Time. Changing travel demand puts different pressures on multimodal networks, particularly during periods of peak demand and system disruptions. This research will identify and develop methods that measure performance across time, with a focus on quantifying performance during peak periods and system disruptions.
Research into Reporting Multimodal System Performance. MSP scores will likely remain relative, requiring some sort of benchmarking to simplify reporting, similar to the highway level of service (LOS) grading system. The research will explore and develop reporting methods for the MSP and related measures, such as network efficiency and resiliency.
Measure Applications Research
The ultimate goal for the multimodal system measure is to provide meaningful feedback to planners, operators, decision makers, and the public. This proposed research would test and refine the measure for use in the following areas:
Research into Transportation System Management and Operations Applications. Operators could use MSP feedback to adjust operations across the system, with a focus on improving resiliency. The MSP is not likely to provide real time information in the near future because of the challenge of collecting count data, but it can pinpoint recent performance issues. For example, MSP scores could identify the advantage of implementing transit pre-emptive signal timing along a congested corridor. The research would focus on how system managers and operators could most effectively use the measures in their day-to-day operations.
Research into Transportation Planning Applications. Planners would use the MSP feedback to identify system problems and properly weigh multimodal improvement options, such as whether to take away a traffic lane for exclusive transit use. The research would identify how the measures could improve the coordination and cross evaluation of travel modes, from a system perspective, in the planning process.
Research into Project Programming Applications. The MSP can provide feedback to decision makers on the relative performance improvements of planned transportation projects. For example, the MSP could become another factor in the Virginia Department of Transportation’s Smart Scale program. The research would identify methods to improve how projects are prioritized across modes and from a system perspective.
Related Area Research
The following research topics focus on how multimodal system performance measurement can support or be supported by other programs, research areas and initiatives.
Research into Multimodal System Performance and System Resiliency. The science behind how management and operations can improve transportation system resiliency is under development and there is a clear relationship between that science and multimodal system measurement. This research would identify the synergies of data collection and analysis methods developed under both initiatives, such as the coordination of crowdsourced and travel data collection efforts.
Research into Multimodal System Performance and System Planning and Programming. The complexity of transportation planning and programming continues to increase yet performance metrics have not kept pace. As a result, many State Departments of Transportation (DOTs) and Metropolitan Planning Organizations (MPOs) are reluctantly planning and programming with outdated measures or developing new measures on an ad hoc basis. The research in this research project illuminated several important dynamics of multimodal system measurement, such as the need for and value of complete person trip data and a place-based perspective that could influence how transportation planning and programming are done. For example, place-based assessments could improve the integration of land use and transportation planning and with it, relationships between transportation agencies and local government. This area of research will explore the relationships between multimodal system measures and transportation planning, such as how such measures would influence the development of a MPO long-range transportation plan or possibly a local government comprehensive plan.
Research into the MSP and Travel Demand Modeling and Forecasting. A technical relationship already exists between the MSP and travel demand forecasting models used for transportation planning. Transportation models have long generated complete person trip information, yet despite this richness of data from models, it reflects the single-mode, facility-based orientation of the profession, that multimodal system measures have not been developed. Once multimodal system data and methods "catch-up" with travel demand models, it will be possible to evaluate performance seamlessly in the past and the future. This area of research will identify how to integrate the data and methods across both platforms, most notably, how multimodal system measurement data can improve the calibration of forecasting models.
Research into the MSP and Other Performance Measures. Although the MSP will add a new, more integrated perspective to performance measurement, it does not preclude the need for currently used mode specific, facility-based measures, such as highway level of service, and system measures, such as vehicle miles traveled. As noted earlier, facility-based measures can help diagnose the reasons for poor multimodal system performance. This research will identify how multimodal system performance and other measures can be used in concert to improve planning and operations.
Summary
The innovation and research projects listed above are proposals developed from the insights gained from this research effort. The FHWA will follow its normal research development process and may undertake some of the proposed innovation and/or research topics identified in this report in the future. As of fall 2018, FHWA has identified a follow-on study for potential funding.
