Practices on Acquiring Proprietary Data for Transportation Applications (2019)

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1 Data are a critical input into the effective planning, design, operation, and maintenance of transportation infrastructure. Agencies have been collecting various data items to support their planning, design, operation, and maintenance functions. Recent techno­ logical advancements have led to new types of transportation data that can provide insights into a wide range of travel characteristics. For example, GPS­based vehicular location data generated by commercial fleets and passenger automobiles with in­vehicle navigation systems have been obtained and processed by third­party data providers into various prod­ ucts such as speed, origin–destination (O­D), and volume. These data items contain rich information on uses and conditions of the transportation system for both motorized and non­motorized modes. They often provide greater temporal and wider geographical coverage than the traditional data sets. State departments of transportation (DOTs) and metropolitan planning organizations (MPOs) have recognized the potential of these data. Many have begun using these data sets for their transportation applications. The primary objective of this study is to compile and review practices that state DOTs and MPOs have leveraged to acquire and use proprietary data. The focus is on those data generated by technologies such as GPS, mobile phones, or crowdsource travel alerts. Several approaches were used to gather information for the study. A comprehensive literature review was performed to look at past studies involving data procurement and to establish what types of data agencies have obtained and determine their uses. An online survey was designed to seek information on three major aspects of proprietary data: (1) data items acquired and applications, (2) procurement method, and (3) use experience. The survey was distributed to state DOTs via an email distribution list with the assistance of the AASHTO Data Management and Analytics Committee. For states that are not on the distribution list, the study team identified DOT personnel in the area of data management, planning, or operations through an online directory search. All 50 state DOTs were invited to participate in the survey. The survey was also distributed to 22 large MPOs with populations of more than 2.5 million. Forty­two state DOTs and three MPOs responded to the survey or participated in phone interviews. Of those states that responded, 79% indicated that they have acquired at least one proprietary data set. The research team also interviewed state DOT staff from Ohio, Wisconsin, Arizona, and Kentucky—as well as staff from the Atlanta Regional Commission in Georgia—to gain detailed knowledge on agency practices and perspectives on procuring and using these data. The study found that unmet needs for data and new insights offered by proprietary data are the main driving factors that prompt transportation agencies to acquire proprietary S U M M A R Y Practices on Acquiring Proprietary Data for Transportation Applications

2 Practices on Acquiring Proprietary Data for Transportation Applications data. Among the data that have been acquired, speed data are being used widely by trans­ portation agencies around the United States for a variety of applications and have been integrated into mainstream agency business areas by some agencies. Numerous uses have also been found for O­D data enabled by highly precise GPS data from in­vehicle systems and mobile phones. A growing number of agencies are partnering with Waze under its Connected Citizens Program and incorporating Waze incident and jam alerts into their traffic monitoring and reporting services. Crowdsourced smartphone applications also benefit from data collection for bicycle and pedestrian trips. Several agencies have begun to leverage this emerging data source to better understand cyclist and pedestrian patterns and to make informed decisions about infrastructure investment. Many agencies have also procured socioeconomic, employment, and freight movement data—as well as digital maps and aerial imagery data—to support transportation applications. The study also found that most procurements were directly handled by transportation agencies, while some were handled by consultants (including universities). The survey respondents and interviewees identified several barriers and concerns associated with these proprietary data and shared their perspectives and practices as they relate to these concerns. Data and service quality: Because the data are passively collected from GPS­enabled devices and processed using the vendor’s proprietary method, there is a wide range of concerns over sample size, demographic and geographic biases, accuracy, and data latency for time­sensitive applications. The identified practices to address issues of data and service quality include the following: • Include clear data specification in the request for proposal (RFP), including temporal and spatial coverage and sample size requirement; • Request sample data from vendors for evaluation; • Consider customer service during vendor selection; • Include a service­level agreement in the RFP and the contract; and • Specify the exit strategy in the contract. Cost: Agencies indicated that acquiring proprietary data can be expensive and reported the following practices and perspectives: • Request detailed cost structure from vendors, including future renewal pricing options; • Use the standard cost sheet to facilitate comparison among vendors and products; and • Coordinate with internal units, and collaborate with partnering agencies on data licenses to achieve economies of scale. Staff expertise and IT resources: These proprietary data are often in large quantity and in a different format, resolution, and referencing system from the agency’s existing data system. Data storage, processing, and management needs—as well as integration effort requirements—often strain the agency’s information technology (IT) infrastructure and staff resources. Reported agency practices related to this issue include the following: • Involve agency IT and data analysts in the procurement process; and • Consider open source, off­the­shelf tools for data processing needs. Finding the right product: This effort is a challenge as new data and services continue to emerge with advancements in technology. Experience gained by survey respondents and interviewees that may be useful includes the following: • Use request for information (RFI) to gather data on current market; • Promote intra­ and interagency collaboration to coordinate the data need;

Summary 3 • Develop a clear vision on data uses; • Consider analytical tools in addition to data; and • Specify broad agency data needs, and ask vendors to propose services to meet those needs. Legal issues: Agencies may face use and sharing restrictions under the data licensing agreement. There are also privacy concerns, as well as concerns over potential conflict between the non­disclosure agreement and open records laws. Current practices identi­ fied by respondents and interviewees include the following: • Involve agency legal counsel in contract negotiation; • Specify terms of use in the contract, and try to include all agency internal business areas while balancing the cost; • Specify the data­sharing plan in the contract; and • Specify how to handle open records requests in the contract. Additional findings related to procurement practices reported by state DOTs and MPOs are as follows: • Agencies can take better advantage of emerging data sources and more easily navigate intellectual property rights if legislatures revisit and amend existing laws that may restrict or prohibit acquiring or using crowdsourced data collected on the basis of personally identifiable information. • Establish procedures explicitly for proprietary data acquisitions and applications, which should cover data contracting, sharing agreement, and quality­assessment strategies, as well as market evaluation. • Incorporate proprietary data into data business plans (DBPs) as an integral component to fulfill departmental business needs and to fill data gaps. Promote coordination and collaboration among different departments within agencies and other state DOTs to make the best use of agency resources and to reduce the cost of proprietary data acqui­ sition, storage, and sharing. • Identify funding sources for data acquisition. If possible, establish a regular budget to maintain data purchases or subscriptions, given that the data meet the agency’s business needs. • Follow agency guidelines or state regulations, if any, with regard to proposal evaluations. More than two­thirds of the responding states have formal guidelines for evaluating vendors and their products. • Develop a list of follow­up questions for vendors with regard to their original data sources and the methodologies used for processing data. These questions foster transparency and help the agency better understand the strengths and weaknesses of a vendor’s data and approach. • Ask vendors to discuss the integration efforts based on the agency’s need. Request infor­ mation on vendors’ past work on integration with other public entities. Finally, the study identified the following areas of future research based on current gaps in the practice: • Develop standard proprietary data license models and application guidelines for those commonly used data types. • Investigate unit costs of proprietary data. • Develop guidelines and methodologies to help state DOTs and MPOs: (1) validate proprietary data; and (2) integrate the proprietary data with their own network, such as state DOTs’ linear referencing network and MPOs’ travel­demand model network.

4 Practices on Acquiring Proprietary Data for Transportation Applications • Conduct case studies or peer exchange to identify successful practices on proprietary data uses, management, and governance. • Conduct case studies or peer exchange to evaluate the benefits, challenges, and best practices of forming partnerships among agencies—including state DOTs, MPOs, transit agencies, and local governments—to pool resources and share data.