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59 Recent technological advancements have led to new types of transportation data. These data often have greater temporal and wider geographical coverage and may contain more details than the traditional data sets (e.g., probe and crowdsourced data). Despite facing challenges associated with obtaining these new proprietary data, state DOTs and MPOs have been using them to meet various needs. This study compiled information on the practices and experience of state DOTs and MPOs on the acquisition and use of proprietary data for transportation applications. Summary of Findings Data and Uses Survey results indicated that DOTs and MPOs have acquired several types of data, such as real-time and historical speed data; O-D data, including truck trip data; bicycle- and pedestrian- count data; crowdsourced incident and jam alerts; socioeconomic data; freight movement data; and digital map and imagery products. These data are used to support a wide range of agency business areas. Among the data reviewed, real-time and historical speed data are the most widely used by transportation agencies around the U.S. for a variety of applications. Agencies employ real-time speed data to support highway operations, including travel-time monitoring, posting alerts on DMSs, 511 services, and incident recovery monitoring. With respect to traffic monitoring, the use of probe-speed data varies among agencies (Athey Creek Consultants 2017). Some agencies obtain such data only for roads that lack sensors or for specific project needs, while others are shifting to probe-speed data for statewide coverage and only deploy sensors where probe data are inadequate. Historical speed data are often used in applications such as performance measures, corridor studies, before-and-after project evaluations, and travel-demand model validation. Highly precise GPS data from in-vehicle systems and mobile phones have found numerous uses in transportation applications. Vendors are processing these data to provide information on O-D pairsâincluding for trucksâat various spatial and temporal resolutions. These data provide useful information on trip patterns that are not available from traditional data collection methods. However, concerns remain about the potential biases inherent to these data because samples are not randomly selected and can be demographically skewed. These data often require additional staff resources for validation purposes. A growing number of agencies are partnering with Waze under its Connected Citizens Program. These partnerships give agencies access to incident and jam alerts generated by Waze users, which can then be incorporated into the agenciesâ traffic monitoring and reporting C H A P T E R 5 Conclusions
60 Practices on Acquiring Proprietary Data for Transportation Applications services. In exchange, agencies send Waze information on special events and planned road work, which are shared with Waze users. Crowdsourced alerts are likely to become an increasingly important part of providing incident awareness. Crowdsourced smartphone applications also benefit from data collection for non-motorized modes of travel, such as for bicycle and pedestrian trips. Several agencies have begun to leverage these emerging data sources to better understand popular routes on networks and factors that affect cyclistsâ decision making. The analyses help agencies to identify optimal locations for bike counters and to make informed decisions about infrastructure investment. However, issues found in other data typesâsuch as limited sample sizes, as well as demographic and geographic biasesâare also present in the non-motorized data. Many agencies have also procured socioeconomic data, employment data, freight movement data, as well as digital maps and aerial imagery to support transportation applications. These data tend to be licensed from well-established providers. Agency Concerns and Practices Survey respondents and interviewees identified several barriers to and concerns associated with procuring and using proprietary data. They offered reflections on their experiences pro- curing data and shared their perspectives and recommended best practices. Table 8 summarizes these concerns and related agency experiences and practices. Successful Procurement Practices The agency experiences and practices listed in Table 8 address general concerns with regard to proprietary data, while the practices discussed in this section pertain specifically to the pro- curement process. Some practices overlap, but they are discussed in more detail here. Successful practices are summarized in the following categories across different stages in the procurement process. Legislative and Institutional Support â¢ Agencies can take better advantage of emerging data sources and more easily navigate intel- lectual property rights if legislatures revisit and amend existing laws that may restrict or prohibit acquiring or using crowdsourced data collected based on personally identifiable information. â¢ Establish procedures explicitly for proprietary data acquisitions and applications, which should cover data contracting, sharing agreements, and quality-assessment strategies, as well as market evaluation. â¢ Incorporate proprietary data into DBPs as an integral component to fulfill departmental business needs and to fill data gaps. Promote coordination and collaboration among depart- ments within agencies and other state DOTs to make the best use of agency resources and to reduce the cost of proprietary data acquisition, storage, and sharing. â¢ Identify funding sources for data acquisition. If possible, establish a regular budget to maintain data purchases or subscriptions, given that the data meets the agencyâs business needs. â¢ Ensure that agency staff have necessary expertise or skills to acquire and work with proprietary data. This may include training, IT, and legal support. Before Issuing the RFP â¢ Establish a workgroup consisting of staff from different offices and divisions within an agency to identify data needs. Determining to what extent data needs overlap should be a key focus of conversations. Forming workgroups is also useful for making different work units
Conclusions 63 per year. This step offers clarity on expected future costs, allowing agencies to prepare more accurate budgets. â¢ Work with in-house legal counsel during contract negotiations. The goal is for agencies to ensure full compliance with federal, state, and local laws, especially as it relates to how open records requests for proprietary data will be handled. â¢ Negotiate agreements with private vendors to obtain the most favorable terms possible. â¢ Specify and confirm data-sharing policies. â¢ Articulate an exit strategy clearly within contracts. Areas of Future Research Agencies are likely to face similar challenges during data acquisition, validation, and appli- cation. Survey results show that agencies purchased the same or similar data sets for the same intended uses. Agencies that are new to proprietary data acquisition can learn from early adopters. Hence, communication with peer agencies often proves valuable. Peer exchanges can be an effective approach for interagency information sharing. Efforts at the national level may be needed to develop guidance or standardized processes for proprietary data acquisition, validation, and integration. Areas of future research are identified as follows: â¢ Develop standard proprietary data license models and application guidelines for those commonly used data types. â¢ Investigate unit cost of proprietary data based on past procurement to assist agencies in future decisions on acquiring data. â¢ Develop guidelines and methodologies to help state DOTs and MPOs: (1) validate propri- etary data; and (2) integrate the proprietary data with their own network, such as state DOTsâ linear referencing network and MPOsâ travel-demand model network. â¢ Conduct more analyses on bike and pedestrian data. â¢ Conduct case studies or peer exchange to identify successful practices on proprietary data uses, management, and governance. â¢ Conduct case studies or peer exchanges to evaluate the benefit, challenges, and best practice of forming partnerships among agencies, including state DOTs, MPOs, transit agencies, and local government to pool resources and share data. Today, innovation in the technology sector is transforming the field of transportation. As connected and autonomous vehicles and mobility-on-demand services continue to expand their user bases, the data needs of transportation agencies will continue to evolve. In the meantime, new challenges will certainly surface during the process. Prompted by these proprietary data in large volume, many agencies have begun turning toward big data tools or cloud comput- ing services to handle their data processing needs. As noted in NCHRP Synthesis 508: Data Management and Governance Practices (Gharaibeh et al. 2017), this transformation may create additional uncertainties, such as data security risks.