Recent advancements in transportation data collection procedures have led to a plethora of data whose characteristics may include improved quality, greater temporal coverage, wider geographical coverage and differing population characteristics than traditional data sets. New data sources, such as probe vehicles, GPS, crowd-sourced data (e.g., INRIX, HERE, Google, TomTom, Waze, etc.), Bluetooth, cellular data, and other emerging data sources have the potential to generate performance measures needed to perform HCM-related analyses, as opposed to providing estimates, as well as validate analysis results. However, it is not fully understood the extent that these data can be used in highway capacity and performance evaluation procedures. Depending on their granularity and specifications, they could supplement existing high-resolution data or, provide rudimentary performance measures.
There are challenges associated with obtaining this private sector data. Issues include cost, limited availability with strict use policies and non-disclosure agreements, privacy, ensuring the data is useable in terms of quality; including timelines, coverage, and accuracy, continuity, and provenance. Despite the myriad challenges, transportation agencies negotiate with private vendors to obtain consistent, timely, quality data for planning purposes. From the USDOT acquisition of the National Performance Management Research Data Set (NPMRDS) to state DOTs and Metropolitan Planning Organizations purchasing freight databases, practitioners are bombarded with information, but not quite enough to make important decisions regarding spending on data.
The objective of this research project is to investigate the use of non-traditional data sources for conducting highway capacity and quality of service analysis in three areas: field measurement of performance measures, local calibration of HCM models, and suitability to support validation of existing HCM models.
To meet these objectives the study will:
• Identify the available data sources at various levels (private and public sector)
• Identify methods and tools needed to store, process and use the data. As such, the research will determine whether specific data mining or data fusion techniques may be necessary to apply in order to use these data sources
• Summarize how agencies integrate new data sources within the broader data management system, to complement or replace existing transportation data sets
Information to be collected will include:
• Data requirements for the major performance measures and default parameters used in the HCM for different levels of analysis, such as operational, design, and planning and preliminary engineering across the different modes
• Non-traditional data (private, public sector) sources that correspond to HCM data needs
• How data have been used in recent years for research related to transportation performance evaluation
• How DOTs have used/are using non-traditional data sources
A literature review and surveys to all state DOTs and selected MPOs will be conducted to identify how agencies use non-traditional data sources. Case examples regarding how to assess licensing options, caveats and risks, and use cases will be collected from the agencies that have engaged in data negotiations.
• “Turning Data into Information for Transport Decision Making,” Anita Vandervalk, Cambridge Systematics, Inc.
First Panel: October 20, 2017, Washington, DC
Teleconference with Consultant: