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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Suggested Citation:"Chapter 4 - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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45 This chapter examines proprietary data acquisition in greater detail by looking at the policies and practices of four state DOTs and one MPO. The study team collected informa- tion by interviewing agency staff and reviewing agency procurement documents. Case examples are not inclusive of all proprietary data acquired by the agencies. The data and procurement cases presented in this chapter have been selected to reflect diverse practices that peer agencies may find useful. Each case example discusses the following topics: • Procurement method, including RFP and contracting; • Use experience, including use cases and caveats, if any; • Peer advice, including reflections of the agency interviewees on their experience with pro- curement, and advice to peer agencies planning similar acquisitions. Because the narratives for each case example attempt to underscore the unique and interesting facets of each agency’s practices, they review the listed items in the order above. But the amount of detail for each element varies. Ohio DOT Experience The Ohio DOT has acquired abundant third-party data in recent years. Since 2012, the agency has used third-party real-time speed data to monitor traffic conditions on major highways and to track maintenance response and recovery times when speed reductions result from incidents or weather events. In 2017, the Ohio DOT began licensing vehicle O-D data. This case example focuses on these two procurements. Procurement Method Speed data The most recent procurement of real-time and historical speed data was initiated in 2016. A copy of the RFP can be found in Appendix D2. The agency sought real-time speed data for all areas that had been covered by an expiring contract, as well as areas that had not been monitored previously. The RFP asked for historical speed data for at least 15,000 centerline miles, including all state, U.S., and Interstate routes. For real-time data, the RFP stipulated the minimum number of data points required and the minimum spacing between data points. The latter was based on average spacing of roadway segments. More specifically, the agency requested the following: • Update and convey real-time data to the agency in 1-minute intervals between 5 a.m. and 9 p.m. and at a maximum interval of 3 minutes from 9 p.m. to 5 a.m.; C H A P T E R 4 Case Examples

46 Practices on Acquiring Proprietary Data for Transportation Applications • Guarantee data accuracy of ± 4 mph; • Provide access to the analytics tools, including region explorer, massive raw data down- loader, congestion scan, trend maps, performance charts, performance summaries, bottleneck rankings, and user delay cost analysis; • Provide data in XML format and at an interval that would let Ohio DOT’s traffic information systems produce accurate and timely traffic information; and • Make real-time speed data available to the agency’s central control system through a vendor’s server. The RFP stated that Ohio DOT intended to use real-time data for transportation purposes, including the operation of freeway management systems, the OHGO app and BuckeyeTraffic.org (both for color-coded speed range maps), and Ohio’s 511 system. The agency required the right to distribute offline and archived speed data from Ohio DOT’s database to other public agencies and universities. It also requested the use of historical data for any internal purpose without restriction and the ability to share them and any analytics tools with all public entities in the state of Ohio at no additional charge. Proposers were required to describe the methods and technologies they would use to capture real-time data, as well as their qualifications and experience. Proposals were evaluated in the following five areas: • Organizational structure and project experience, • Data service and support, • Number of data points, • Estimated costs, and • Any exceptions the proposer submitted to the contract’s supplemental terms and conditions or proof of concept phases associated with terms and conditions. Origin–Destination data The Ohio DOT experienced firsthand the utility of detailed GPS track data when the agency used such data at a congested interchange to determine the possible causes of significant backups at an off-ramp. Patterns of vehicle turning movements uncovered in these data prompted the agency to adjust the timing and coordination of several signals on the arterial. As a result, this largely cleared up ramp backlogs, eliminating the need for a costly ramp reconstruction to add additional vehicle storage space. In 2017, Ohio DOT issued an RFP for O-D data services because the agency recognized the potential of such data to help identify low-cost solutions for problems that otherwise would be addressed using more costly methods. The RFP requested access to accurate O-D data via online portal by Ohio DOT and any public agencies, such as local government, MPOs, universities, and transit agencies, as deter- mined solely by Ohio DOT. There shall not be any use restrictions to an account holder from an Ohio public agency: While unlimited account access will not be given to private entities, Ohio DOT or an approved Ohio public agency account holder shall be able to provide the OD query results to a consultant(s) or a similar private entity working on their projects solely for use in that project. Approved account holders shall also be allowed to temporarily provide access to the OD data query tool to a private entity (such as a consultant) in order to perform queries solely for the purpose of working on that public agency’s project. At the conclusion of the project work that requires the OD information, access to the OD data will be withdrawn from the private entity. A complete copy of this RFP can be found in Appendix D3. The contract was awarded to the INRIX–StreetLight team for 1 year. Currently, the Ohio DOT is working toward a 1-year contract extension.

Case Examples 47 Use Cases Speed data The main use cases for the real-time speed data are DMS, 511 services, and incident recovery monitoring. The historical speed data set has been used in a wide range of applications, such as before-and-after studies and calibration and validation of statewide and MPO travel-demand models. Origin–Destination data The Ohio DOT and its agency partners have been exploring these data for various uses, including studying several corridors; assessing demand; analyzing weaving movements at complex interchanges and intersections; deriving the 30th highest hourly traffic volume; analyzing traffic patterns associated with high-congestion events; and evaluating travel route choice, trip-level travel-time reliability, and vehicle acceleration profiles (Giaimo 2017B, Parikh 2017, Granato 2017, Bernardin 2017, Coates 2017). Ohio DOT staff have also applied the data in ways they had not anticipated when the agency originally licensed the data, such as evaluating the merits of proposed projects. The Transportation Review Advisory Council reviews capacity expansion projects requested by communities each year. In applications for expansion projects, heavy traffic or high truck volumes are frequently cited as key factors to justify the funding request. Previously, the agency had no reliable way to validate proposers’ claims about traffic conditions. Having access to detailed trip data has equipped the Council with the information necessary to analyze and verify project justifications, which has improved its ability to judiciously allocate funds. The O-D data cannot replace traditional household travel surveys because they do not contain information on trip or traveler characteristics. However, the data can supplement existing data on O-D pairs and turning movements. Analyses performed by Ohio DOT and partners also suggested caution when using and interpreting the data (Giaimo 2017A). In-vehicle GPS devices are the main source of probe data. Although these systems are precise, they are mostly installed in either newer or higher-end vehicles, skewing data on passenger trips because of the overrepresentation of high-income drivers. Likewise, larger commercial vehicle operations— compared to smaller local carriers—use more semi-trucks on which GPS devices are installed. Accordingly, interstates tend to be overrepresented, and local roads are underrepresented. Furthermore, data are collected only from carriers that supply data to the data vendor. LBS from GPS-enabled phones also carry limitations. They only transmit data when the phone’s GPS function is enabled and in use. Although the resulting data are spatially precise, coverage is sometimes sparse, and it is not possible to distinguish cars from commercial vehicles. Nevertheless, there would be less demographic bias because of widespread market penetration of smartphones, and it is possible to infer home and work locations because of long-term device persistence. A final challenge is trip-length bias. With no scientific or experimental design underpinning data collection, longer trips are potentially overrepresented in the data sets. The Ohio DOT has had access to this O-D data for approximately 1 year. Because of the relatively short time period, it would be premature for the agency to perform a full cost–benefit analysis to estimate the return on the investment. Agency staff continue to explore new uses for the data. The data tend to overrepresent some users of the transportation system, which produces demographic bias based on factors such as age, income level, and vehicle type. Never- theless, the data have proven useful for analyzing traffic on low-volume roads to measure crash rates for safety analysis. It would not be cost-effective to install the number of sensors necessary on low-volume roads to produce these estimates, which makes probe-vehicle data an attractive option. The Ohio DOT has not encountered issues over privacy concerns. The StreetLight plat- form, for example, warns users if they attempt to specify a geographic area that is too small or

48 Practices on Acquiring Proprietary Data for Transportation Applications too narrow of a time period. This feature potentially mitigates concerns about particular firms being targeted. Peer Advice Crowdsourced O-D data are relatively new to the transportation field. As a result, there is not a well-established market with well-defined products and competitors in the space. Limited competition in the field for such data can potentially drive up the price and make it difficult to evaluate the cost-effectiveness of acquisition. If an agency would like to acquire such data, it is critical for it to significantly involve its procurement department when devel- oping RFPs. Asking procurement staff for input will help ensure that RFPs comply with internal purchasing guidelines and processes, while at the same time fostering competition among potential proposers. Since this is a rather new data source without well-established use cases, it may take consid- erable time and effort for agency staff to cultivate a vision for how the data can be used. Once the decision is made to acquire the data, agencies may want to consider organizing workshops or seminars to provide training on the data and platform and bringing people from all work units together to identify potential use cases. Those collaborative activities will give agency staff an opportunity to think critically about the data and identify ways in which new data can be analyzed to solve problems. It is essential to raise awareness among staff about data availability and to hold trainings on the data and platform. Wisconsin DOT Experience The Wisconsin DOT administers and maintains Wisconsin’s state highway system, which consists of 11,745 miles of roadways, including 876 miles of interstate freeways. Over the last 10 years, the agency has licensed data from several vendors, including Waze, TomTom, INRIX, and ATRI. These data have supported operations and planning applications, such as real-time incident awareness, speed and travel-time monitoring, and travel-model development. The following discussion highlights the agency’s experience with data acquired from TomTom and ATRI. Procurement Method Real-time traffic data The Wisconsin DOT began licensing real-time probe-speed data in 2015. Procurement was motivated by the need to acquire reliable speed data as part of its ambitious I-39/90 Expansion Project, which is reconstructing a 45-mile interstate corridor that extends from the Illinois state line to the US 12/18 interchange near Madison. Anticipating that the project would take 6 years to complete, Wisconsin DOT needed to monitor traffic con- ditions on the construction sites, as well as the arterials in the area. The goal was to identify and publicize alternative routes for periods when congestion produces lengthy delays through the freeway corridor. Acquiring probe-vehicle speed data was deemed more economical than deploying permanent sensors for traffic monitoring, especially given that—while construction is temporary—instruments would incur long-term maintenance costs, in addition to the initial deployment cost. Since some arterials are not state roads, the DOT would have had to go through the process of signing a memorandum of understanding with local agencies and municipalities before deploying sensors on these roads. The Wisconsin DOT’s RFP solicited mean- and median-speed data for all roadways and routes expected to carry increased traffic during the I-39/90 Expansion Project. The RFP stated that

Case Examples 49 all data received from the selected vendor would be post-processed by the agency’s STOC. Once processed, it would be used to alert the public of traffic conditions via DMSs and the agency’s 511 website. The RFP specified that data were to be provided as XML-formatted content so they could be incorporated into the STOC ATMS. Data were to be received in 1-minute intervals, and vendor assistance with integrating real-time traffic data into Wisconsin DOT’s current systems while enhancing or extending the agency’s real-time traffic services was requested. The RFP also stipulated that Wisconsin DOT would retain the right to archive and use all data conveyed to it perpetually for analyses and research purposes. Wisconsin DOT listed value-added features it wanted to see included in proposals (e.g., specific routes, potential for tiered pricing if centerline miles provided reached a certain threshold, and adjustable segment lengths). All proposals, the RFP stated, would be evaluated in three areas: proposer information and solutions, including organizational capabilities, staff qualifications, proposed solutions, licensing, and references; contract requirements; and the cost proposal. TomTom was selected to provide travel-time data and services. The initial contract was for a 2-year fixed term with the option to renew for up to 5 years. The contract calls for providing data on approximately 200–300 miles of roads within Rock and Dane counties. However, it grants the possibility of future expansion to cover additional roads. ATRI truck GPS data In 2014, Wisconsin DOT entered into an agreement with ATRI to make a one-time purchase of truck position data for the month of April 2014. The agency licensed data through its contractor, Cambridge Systematics, to facilitate the update of the statewide travel-demand model, and specifically for the preparation of an O-D truck table. Cambridge Systematics recommended contracting with ATRI for data, given its previous experience with working with other state DOTs on truck travel models. The ATRI truck data contains GPS tracks of a truck (with unique ID) as it travels through the roadway. They were collected as part of the Freight Performance Measures Initiative (FPM), in collaboration with FHWA. The data provide a sample of truck movements across the state and can shed some light on the origins and destinations of these trips. The agreement restricts the use of these data, mandating that they are “only for the purpose of monitoring and assessing truck travel patterns and truck trip modeling within the state of Wisconsin.” It also sanctions assessments of truck travel patterns to measure highway travel times. Wisconsin DOT agreed that it would not use the data to create carrier- or shipper-specific data, nor can the agency distribute data to other outside parties that have not signed the ATRI data-sharing agreement (unless compelled to do so by court order pursuant to Wisconsin public records law). However, the agency can present processed FPM data and analyses in aggregated, visualized formats. Negotiations pertaining to the agreement centered on two issues: (1) How to handle open records disclosure and (2) How to handle trade secrets. Wisconsin DOT’s Office of General Counsel (OGC) participated in the negotiation. Wisconsin public records law states that “except as provided by law, a requester has a right to inspect any record” kept by an authority. The OGC viewed the vendor’s business and product information as records kept by the Wisconsin DOT, and any confidentiality agreement with the data vendor would not qualify as an excep- tion “provided by law” that would justify withholding records in Wisconsin DOT’s custody. Therefore, such an agreement would conflict with the state’s open records law. However, OGC proposed a compromise solution—pursuant to Wisconsin Statutes § 19.36(5) and § 134.90(1)(c)—that would treat FPM data as a trade secret unless specifically designated otherwise by ATRI. Wisconsin Statute § 134.90(1)(c) defines a trade secret as information that derives independent value, actual or potential, from not being generally known to, and not being readily ascertainable by proper means by, other persons who can obtain economic value

50 Practices on Acquiring Proprietary Data for Transportation Applications from its disclosure or use. Under Wisconsin Statute § 19.36(5), Wisconsin DOT has standing to withhold records from public inspection if they are deemed trade secrets prior to judicial review. The final agreement between Wisconsin DOT and ATRI holds that Wisconsin DOT recognizes ATRI’s truck GPS data as a trade secret that can be withheld under Wisconsin Statute § 19.36(5), unless there is a finding by a Wisconsin court of competent jurisdiction. Wisconsin DOT is responsible for notifying ATRI if data are requested by an outside party. It is then incumbent upon ATRI to take legal or other action in a manner consistent with Wisconsin’s public records law to argue against its disclosure before Wisconsin DOT would make it available. The agreement also states that Wisconsin DOT shall work with ATRI to develop and complete data-sharing agreements with any other parties (e.g., contractors doing work for the Wisconsin DOT) before ATRI data can be distributed. Use Cases Real-time traffic data As part of its effort to meet the requirement of Section 1201 of the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users for a real- time system management information program, Wisconsin DOT needed to provide information on significant, non-interstate routes in the southeastern part of the state and in the Milwaukee metro area. The agency expanded the contract with TomTom to cover speed and travel time on these roads. The agency’s ATMS software uses probe data to calculate travel times on predefined route segments. Such information is then posted to the DMSs and the travel-time list on the agency’s 511 system at 511WI.gov. Route travel times are also archived by Wisconsin DOT for future use. TomTom’s data are attached to the OpenLR system, an open standard-location referencing system that uses a starting point, end point, and at least one intermediate point to delineate a road segment. Linking this system with Wisconsin DOT’s linear referencing system was neces- sary to ensure accurate spatial attribution of traffic conditions. The initial integration of the network into ATMS software took several months. However, periodic network updates by the vendor would require the Wisconsin DOT to make manual adjustments to ensure a proper match of the links. Probe-speed data have proven reliable based on validation using floating cars. Wisconsin DOT has renewed the license and expanded the data coverage to include roads in seven counties. The primary use of the data remains as inputs to ATMS to generate travel-time estimates on predefined routes to be displayed on DMS and on 511WI’s travel-time list. The agency can also query archived travel speed for the entire state and use the results for other applications. ATRI truck GPS data The ATRI truck data consist of GPS records within an approximately 7-mile buffer around the state. The purchase encompassed 1 month of truck data, which Cambridge Systematics used to develop O-D estimation data that, in turn, were used to categorize all O-D trips as short-distance closed tours (which refer to trips generally less than 100 miles that occur in a single day and have the same start and end points) and long-distance open tours, which can span hundreds of miles and whose starting and ending locations differ from each other. Although the consultant processed all raw data, Wisconsin DOT engineers believe the final truck O-D table appears to be in line with those derived historically. The data have proven useful for understanding and modeling freight movements in Wisconsin. As such, Wisconsin DOT views the purchase as being cost-effective and conferring significant benefit to the state.

Case Examples 51 Peer Advice Obtaining probe data to monitor traffic conditions is a much less-expensive option than installing sensors on roadways in areas under travel advisory during freeway construction. Agencies can modify their probe data coverage in the future if needs change; whereas, perma- nent sensor installations do not afford this flexibility. Wisconsin DOT staff emphasized the importance of setting realistic expectations when initially establishing a contract with a new vendor. Once a vendor has been selected, agencies should anticipate devoting significant time to becoming familiar with the data and learning how to work with them. Before choosing a vendor, it is also critical for an agency to consider its potential customer service needs and to select a firm whose customer service performance is well documented and aligns with its requirements. When procuring proprietary data, involving an OGC during contract negotiations is critical. This ensures that Wisconsin DOT’s contracts with data vendors fully comply with state laws, and it offers reasonable protection to sensitive information. Arizona DOT Experience In 2017, the Arizona DOT initiated an effort to procure third-party data for various transpor- tation applications. The adopted approach is innovative in that it enables the agency to award on-call contracts to multiple vendors. This master on-call agreement allows any public entity in the state (e.g., local agencies and MPOs) to enter into data licensing agreements with any of the selected vendors without going through a separate RFP process. Procurement Method Instead of seeking data from one vendor or vendor team, the Arizona DOT issued an RFP for multiple on-call contractors. The Systems Technology Development manager spearheaded this effort. He championed this approach because the Arizona DOT anticipated its data needs will evolve in the future, given the rapid changes in technology and, consequently, the data market. The agency believed this contracting strategy would offer significant flexibility because it does not tie the agency to a single vendor or service. With two or three on-call vendors, the agency could leverage the unique features of different products and services from different vendors to meet various needs. The Arizona DOT requested statewide, regional, and local corridor-level traffic data for all purposes. Data were to cover the state freeway system, state and U.S. routes, arterials, and local routes. It solicited a wide range of data and services, such as: • Historical traffic information, • Traffic-volume counts, • Travel time information, • Traffic analytics, • Predictive traffic, • Traffic-pattern data, • Performance measures, • Mapping data, • User interface that supports the visualization of real-time travel data, • Archived data, • O-D data, and • Any future services or new features that vendors would like to propose.

52 Practices on Acquiring Proprietary Data for Transportation Applications Planned uses of the data included providing traveler information to the public, obtaining and archiving historical traffic data to inform agency planning, tracking historical changes in traffic volumes, and using traffic data to report performance and support the operation of freeways and the arterial system. In addition to describing the Arizona DOT’s data needs, the RFP stipulated that the winning contractors shall be available to all jurisdictions within the state of Arizona that request traffic- data services and that utilize federal, state, city, county, or any other jurisdictional funding for such requests. With a statewide on-call contract, local agencies can directly request data and services through purchase orders. Representatives from Arizona DOT, Maricopa County DOT, and Maricopa Association of Governments participated in the RFP development and proposal evaluations. HERE and INRIX were selected as the on-call vendors. A complete copy of the RFP can be found in Appendix D4. Arizona DOT officials highlighted the potential benefits of using statewide on-call contracting. Selecting two vendors opened access to an array of data products and services, each having different strengths. Including vendor base pricing in the master contract enhanced the trans- parency of the products and services offered, as well as pricing mechanisms. Agencies can use this knowledge to more accurately budget for project costs. Use Cases Since awarding the master contract, the Arizona DOT has ordered several products, including INRIX Roadway Analytics, INRIX Real-Time Traffic Flow, and StreetLight InSight (a cloud- based platform for transportation analytics). INRIX Roadway Analytics are used to identify bottlenecks, to calculate performance measures (e.g., delays, vehicle miles traveled, and vehicle hours of delay), and to perform safety evaluations. Speed and travel data at 1-minute increments obtained through INRIX Real-Time Traffic Flow have been used to improve dynamic monitor- ing of traffic conditions. StreetLight InSight data have been employed to generate O-D tables for commercial trucks and personal vehicles and to develop travel-demand models. Other public agencies are currently in the process of obtaining services from INRIX and HERE, based on their needs. Peer Advice Arizona DOT officials provided a number of suggestions that other agencies can consider when procuring third-party data. Before issuing an RFP, it is critical to identify big picture data needs and opportunities and to determine whether a joint effort for data acquisition is appropriate. It is useful to put together a working group with transportation professionals from state agencies, MPOs, cities, and counties to coordinate and discuss data needs. Pooling funds to license data would ultimately be a cost-effective strategy for obtaining data, but it can take a significant amount of time and effort to coordinate. The RFP should specify that the contract will apply to all state agencies, universities, MPOs, cities, towns, and counties. This detail ensures that the procurement results in a contract that benefits all partners. It is also important to widely advertise the RFP for a significant period of time to reach as many potential vendors as possible. Ideally, a proposal review committee should include state DOT, MPO, city, county, and FHWA personnel. The Arizona DOT’s statewide on-call contracting is a novel approach to procuring third-party data. There were challenges and obstacles to work through because all parties involved in the process were not experienced in seeking multiple on-call contractors to provide the same service. The Arizona DOT benefited from clearly stating—during the early stages of procurement—that

Case Examples 53 both the agency and taxpayers would benefit from making data acquisition more efficient and giving multiple jurisdictions access to data. Strong partnerships between the Arizona DOT and MPOs and other local agencies have also facilitated adoption of the on-call contracting strategy. Kentucky Transportation Cabinet Experience Since 2012, KYTC has acquired historical speed data to support various transportation appli- cations. The initial procurement was motivated by the need for reliable data to produce travel time-based highway performance measures. Other uses for the data have emerged since then, including travel-model validation, corridor studies, statewide network screening, and project selection. The historical speed data are referenced to a high-resolution street network provided by the vendor. KYTC was able to obtain data for roads outside the coverage of NPMRDS. In 2015, KYTC began licensing real-time speed data for reporting travel conditions and pro- viding real-time incident information to the public. Procurement Method KYTC acquires historical probe-vehicle speed data through the KTC, a non-academic research center housed in the College of Engineering at the University of Kentucky. KTC is the designated research arm of KYTC under the Kentucky Cooperative Transportation Research Program. Acquiring data through a university partner allows KYTC to leverage technical capa- bilities at KTC, where researchers evaluate, process, and integrate the data to support various applications. KTC also creates statistics and reports that are shared with KYTC, MPOs, and other interested parties. A data acquisition committee was formed during the initial efforts to procure data. It includes KTC researchers, KYTC engineers, and the University of Kentucky’s purchasing officer. The committee has prepared RFPs, evaluated submitted proposals, and assisted with contract negotiations. Proposed data sets have been evaluated primarily based on roadway mileage covered, spatial resolution, and temporal adequacy. Sample data have been requested as part of the evaluation process. All data acquired under the contract are licensed by KTC and the University of Kentucky and can be used to support all KYTC applications. Although raw data cannot be shared, KTC can distribute aggregated statistics, reports, and other derivative products to other agencies and the public. Use Cases Historical speed data Historical speed data are used for performance tracking of Kentucky highways over time. Congestion measures and travel-time reliability have been calculated for interstates, NHS roads, arterials, and collectors. Despite sparse coverage on some rural low- volume routes, the data have provided information on the operating conditions of roadways not covered by sensors. Researchers at KTC evaluated data quality and conflated vendor-supplied street networks with Kentucky’s Highway Information System network to join the attributes from the two data sets. Network conflation requires significant effort. Regular maintenance will be needed in the future as vendor and KYTC networks undergo periodic updates. Recently, KYTC introduced the Strategic Highway Investment Formula for Tomorrow model. Its aim is to quantitatively assess and compare the benefits of proposed projects. One of the seven measures in the funding formula is congestion. Measures based on probe-speed data

54 Practices on Acquiring Proprietary Data for Transportation Applications reflect traffic congestion better than traditional measures such as volume-to-service flow ratio. Analysis of probe-speed data by the KTC–KYTC team is being used to develop comprehensive sets of congestion measures for statewide network screening and project selection. Data are also used for MPO congestion management programs, travel-model calibration and validation, bottleneck identification, and air quality analysis. Caution is needed when interpreting data, especially for roads with limited data coverage. For example, low speeds on some rural roads in the mountainous area may not denote congestion. They may be the product of heavy trucks traveling on steep grades, which are abundant in the eastern and southeastern portions of Kentucky (this condition is an issue for real-time data, as well). Thus, one must be mindful of the context in which speed data are collected before drawing conclusions. Historical speed data have enabled new forms of analyses. However, processing such a large volume of data initially strained KYTC’s computing infrastructure. A system upgrade to a Hadoop cluster greatly enhanced the agency’s capability in handling big data projects, such as processing, analyzing, and disseminating data in real time. Waze and real-time speed data Prior to acquiring real-time speed data, the traffic man- agement center in Louisville, Kentucky—called Traffic Response and Incident Management Assisting the River Cities (TRIMARC)—relied primarily on fixed-location sensors, such as radar and microwave, to monitor operating speeds on major highways. Real-time probe-speed data are used at the TRIMARC operations center mainly for posting travel times to DMS throughout Louisville and northern Kentucky. Limited floating car runs were used to validate data, and the results were satisfactory. Probe-speed data provide better coverage on roads outside the Louisville metropolitan area, where sensors are less prevalent. Furthermore, under the agreement KYTC can use the data for internal purposes, save and archive data for future uses, and package data for reporting. The agency can also share data with its contractors. KYTC also established a partnership with Waze through the Connected Citizens Program. Under the Connected Citizens Program agreement KYTC signed with Waze, KYTC shares information with Waze about planned road closures and construction events. In return, KYTC can access information from Waze users about road conditions, such as accidents, potholes, traffic jams, and other hazards. At the statewide level, Waze data and real-time speed data mainly support KYTC operations in the areas of incident detection and management, traffic monitoring and management, tracking vehicle speeds during winter weather events to gauge the effectiveness of snow and ice removal activities, and deciding on the warnings or instructions to place on DMSs. Having access to these data, KYTC reinvented its 511 traveler information system. Before acquiring real-time speed and crowdsourced data, the state’s 511 system relied on telephone-based operations. With its advanced IT infrastructure, KYTC now combines various data sources to generate a com- prehensive picture of real-time conditions throughout the state’s roadway network. Key data sources include the following: • HERE real-time speed data, • Waze incident and jam reports, • Waze traffic viewer, • Twitter, • Doppler radar, • KYTC’s traffic operations center, • TRIMARC incident reports, • snow plows,

Case Examples 55 • internal crowdsourced activities throughout the state, and • DMS. KYTC aggregates data from these sources and harmonizes them based on location and time. KYTC staff can select any district, county, route, or mile-point range and observe all available real-time data at the KYTC’s disposal for that location and the chosen timeframe. Information on alerts and delays from the real-time data are now used to populate GoKy.ky.gov—the state’s real-time traffic information map—with the most up-to-date traffic conditions. Figure 12 shows the map and data view for downtown Louisville. Users can toggle operational layers on and off, examine traffic flow patterns, identify the location of DMSs, and view weather updates from the National Weather Service. In 2016, KYTC phased out its telephone-based 511 system and shifted all data management to in-house staff, saving the agency $750,000 per year. Real-time speed data—coupled with immense computing power—have greatly enhanced KYTC’s operations. KYTC staff use these data to generate novel insights about traffic events that would otherwise be expensive to obtain. For example, Figure 13 shows variations in vehicle speeds resulting from a major crash. These data provide a holistic view of the crash and facilitate the after-action review for the incident management program. Aggregating, processing, and publishing data in real time helps KYTC quickly re-create events surrounding a particular incident—or an entire day—and determine the factors that Figure 12. Screenshot of GoKy.ky.gov website.

56 Practices on Acquiring Proprietary Data for Transportation Applications contributed to its occurrence. This information facilitates after-action reviews and strengthens KYTC’s incident management program, helping the agency improve its responses to future incidents. Large data sets, which integrate the perspectives of multiple roadway users, also foster a more complete representation of incidents in real time. Peer Advice Agencies should have a clear vision on how data will be utilized when preparing RFPs and evaluating data products. KYTC is interested in a reliable data source with a finer spatial granu- larity than the TMC segmentation. Speed data referenced to street network links have proven useful for a number of applications. Such data tend to be very large in size and will require extensive effort to integrate with the linear referencing system used by the agency. Nevertheless, a partnership with KTC has enabled KYTC to leverage technical expertise beyond agency staff resources for procuring, processing, integrating, and analyzing these data. Processing and displaying real-time data can be challenging. Lags in real-time data may be propagated by the processing time required. Complications, such as multiple reports of the same incident, often require additional verification before the data can be used for analytical purposes. Despite these obstacles, KYTC has been satisfied with the quality of the real-time data it has received, as well as the willingness of data providers to continually work with the agency staff to streamline the data delivery. Utilizing open-source parallel computing architecture and off-the-shelf tools has helped KYTC process and integrate various data feeds for traffic and incident monitoring in real time. However, it may be challenging to integrate this advanced architecture with the conventional information technology systems. Figure 13. Crash timeline and impact on speed.

Case Examples 57 Atlanta Regional Commission Experience The Atlanta Regional Commission (ARC) is a federally designated MPO that collaborates with state and local transportation agencies and governments to produce and manage the Regional Transportation Plan. Several groups within ARC’s Center for Livable Communities are dedicated wholly or in part to transportation planning and analysis, including Transportation Access and Mobility, Mobility Services, and Research and Analytics. The Research and Analytics group supports the other groups by collecting data on issues such as demographics, land use, health, transportation, and crime. They perform economic and land-use modeling and geospatial analysis and generate statistics to inform various transporta- tion analysis and planning activities carried out by other ARC groups. The Mobility Services group develops the Transportation Demand Management Plan. This plan focuses on incorpo- rating demand management strategies into planning, project development, and decision making related to system operations investments. The Transportation Access and Mobility group works on issues related to the regional transportation plan, the transportation improvement program, transportation modeling, performance analysis and monitoring, transit planning, and outreach. With groups being responsible for different areas, they have both distinct and overlapping data needs. Procurement Method Data have typically been obtained to meet specific project needs at ARC. The planners and technical staff at ARC take active roles in identifying their data needs and researching the market and the data sets for their applications. They enlisted help from a consultant the first time they acquired probe-vehicle speed data. Subsequent acquisitions have gone through a sole-source acquisition process. According to ARC rules, sole-source acquisition requires proper justification. In the cases of these data licensing contracts, the oft-cited justification is that goods and services are only available through one source. ARC staff conducted significant research on the data and the market, and they prepared documentation on why it is in the best interest of ARC to directly work with a particular provider without the formal RFP process. Use Cases Historical speed data ARC began licensing probe-speed data in 2012 to develop mobility performance measures and to validate travel-demand models. Before licensing the data, ARC retained a consultant to evaluate available data and marketplace conditions. Based on the evaluation, ARC licensed with a vendor to provide probe-vehicle speed data. Since then, ARC has switched to another provider, citing more expansive data coverage and access to user- friendly tools. Integrating the data with the existing data system was a necessary but also very challenging task. There are several versions of the network maintained at ARC and Georgia DOT. ARC’s modeling group maintains a network model in Cube, while the Georgia DOT maintains a linear referencing system for the statewide highway network. The task of linking the vendor’s network— which the probe-speed data references—and Georgia DOT’s linear referencing system network was extremely time-consuming. Although the NPMRDS provides good information on major roadways, ARC still needs data for roads outside the NPMRDS network. Origin–Destination data Recently, the Mobility Services Division licensed O-D data from StreetLight. These data are intended to illustrate trip patterns in more than 900 census tracts

58 Practices on Acquiring Proprietary Data for Transportation Applications around the Atlanta metropolitan area and the seven Transportation Management Association territories. Staff in the Mobility Services Division developed the data specification and worked with the budget office to complete the acquisition. ARC licensed monthly O-D data based on the agency-specified O-D zone structure. The query does not directly provide trip volume because the GPS devices that are part of the data source do not represent a 100% market penetration. Additional processing using traffic-count data is needed to derive O-D volumes. There is some concern over potential bias toward loca- tions where cell phone activity is dense. However, the agency has been very satisfied with the information provided by the data and the ease of integrating results into the GIS platform. Most recently, the Center for Livable Communities began licensing O-D data from AirSage, a firm that collects and analyzes mobile phone signals; GPS; and other locational data to under- stand traffic movements. ARC intends to use the data to better understand external travel patterns for the region. With a 25% surcharge, the data licensing agreement allows ARC to treat these data as open data and to distribute them freely without restriction. Peer Advice Technological advancements have been continually generating new data and products. ARC recognizes that its data needs are evolving and that the landscape of the data market is also evolving, with new products entering the market from time to time. It is challenging to budget for the data acquisition cost in advance. If an agency contains multiple divisions that have a blend of unique and overlapping data needs, internal stakeholders should talk through their data requirements and determine whether it is possible to improve coordination to garner the best possible returns. It would usually be more economical to acquire a data set that can be used by multiple groups rather than a cheaper but more restrictive data set that can only be used for one purpose. On a similar note, ARC staff cited the importance of building strong collaborative relationships with local partners, such as state DOTs, local transit agencies, and local governments. Coordinating with such entities potentially fosters partnerships for sharing the cost of licensing data under regional sharing agreements. ARC recently negotiated a regional license for REMIX, a software tool for transit planning that will give access to ARC, the Metropolitan Atlanta Rapid Transit Authority, and several local transit agencies. Each agency would pay less under the regional license compared to the individual licenses they previously held. On a broader scale, ARC staff emphasized the importance of establishing a clear vision for how data are going to be used. Identifying questions or problems that are to be addressed before licensing data will help agencies choose the most appropriate data sets for their needs. ARC recognizes that it is challenging but important for agencies to keep up with the latest advances in technology, market conditions, and data availability. It is important to engage in continual dialogue with potential data providers with regard to agencies’ data needs and formatting requirements. Communicating data needs to multiple providers fosters healthy competition when agencies solicit proposals for data, potentially lowering the cost and increasing the value- added benefits vendors are willing to offer.

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TRB’s National Cooperative Highway Research Program (NCHRP) Synthesis 541 explores how state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) acquire proprietary data for transportation applications.

Recent technological advancements have led to new types of transportation data with characteristics that include improved quality and greater temporal and wider geographical coverage than traditional data sets. State DOTs and MPOs face challenges associated with obtaining new proprietary data.

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