OBJECTIVE
The objective of this research is to develop tools, methods, and guidance for improving data integration, sharing, and management practices to enable transportation agencies, in collaboration with private-sector and public-sector stakeholders, to make better planning and operations decisions. Secondary benefits will be increased uniformity of data across states and improved consistency of practice.
STATUS
RESEARCH PLAN
The work plan will be divided into five phases. Phase I must be completed before subsequent phases commence. Phases II-V are not necessarily sequential.
RESEARCH PLAN
Phase I—Information Gathering and Planning. This phase use a variety of techniques to gather background information on the topic.
Task I.1. Attend and participate in TRB’s Data & Performance in Transportation Decision-Making Conference. The research team will use this opportunity to identify the latest techniques and individuals to interview.
Task I.2 Conduct a comprehensive literature review. Domestic and international research and development efforts and standards will be reviewed.
Task I.3. Conduct a wide range of comprehensive interviews to:
· Document lessons learned from previous data efforts,
· Identify common data landscapes and good data practices for representative use cases,
· Document challenges and barriers from a few agencies that have struggeled,
· Document private-sector opportunities and challenges,
· Identify future disruptors, and
· Document lessons learned from other industries.
Task I.4. Develop descriptions of representative planning and operational use cases that would benefit from improved data sharing, integration, and management practices.
Task I.5. Develop a data catalog and ontology. This development will use a bottom-up approach that analyzes existing data to discover data models and crate metadata and relationships using a combination of humans and AI technologies. The data catalog will be a valuable resource for analysts and others trying to find data to build insights, discover trends, and identify new products for their agencies. The data catalog will be used to develop an ontology covering the domains defined by the representative use cases from Task I.4.
Task I.6. Based on Tasks I.1 through I.5, identify barriers and gaps in existing practices and recommend new tools, methods, and guidance for data integration, sharing, and management. The potential value of the tools, methods, and guidance will be articulated as the level of effort required to develop them estimated.
Task I.7. Develop an interim report summarizing Tasks I.1 through I.6 and meet with the panel to discuss it and the plans for subsequent phases.
Phase II—Documentation of Best Practices and Lessons Learned. During this phase, the best practices and lessons from Phase I will be made available in an interactive web-based compilation.
Task II.1 Create a centralized, relationally-linked, web-based resource library of relevant knowledge, use cases, data sets, metadata, and tools. Design and development of the library will use Agile and Lean User Experience (UX) methodologies. The website may include article-style posts, cross-linked content, glossary, classification approaches, and a search engine optimization friendly structure.
Task II.2. Develop an Executive Summary that will be prominent on the library website.
Task II.3. Review, revise, and publish the website. This will be an iterative process to obtain a minimum viable product which will then be made publicly available. During the remainder of the project, the website will be monitored and continually improved.
Phase III—Development of Products. This phase will develop new products that address the barriers identified in Phase I and improve agencies’ abilities to use new data approaches.
Task III.1. Following consultation and agreement with the NCHRP and the project panel, develop draft products that fill gaps and convey the unique nature of modern data approaches, including distributed and asynchronous data management and integration.
Task III.2. As the draft products are developed, they will be shared with the panel and partnering agencies for review. Web conferences will be conducted as needed to facilitate the review. Following the reviews, the products will be revised.
Phase IV—Proof of Concept. This phase will validate the usefulness of the products to agencies.
Task IV.1. Develop implementation plans for each product that reflect the varied needs of organizations across data and institutional capabilities. The plans will also describe how the product will be moved from testing, verification and validation, refinement, web-based debut, and broader outreach through industry.
Task IV.2. Test products with a variety of partner agencies over a five-month period. During this time, the contractor will provide support and assistance to the agencies.
Task IV.3. Upon completion of Task IV.2, a verification and validation workshop with up to 15 participants (plus the project panel) will be conducted.
Task IV.4. Revise/refine products and incorporate into website.
Phase V—Deployment. The final phase will bring the project deliverables to the industry.
Task V.1. Develop marketing and outreach strategy and materials that include high-level summaries and product guidance. The products will then be rolled out, primarily through the website, email, and webinars.
Task V.2. Provide deployment assistance to a limited number of states over a six-month period to affect change in the industry.
Task V.3. Explore alternative support structures for states to continue to implement and use the products beyond the NCHRP 08-119 effort.
BACKGROUND
Planning and operating transportation systems involves the exchange of large volumes of data. The lack of common data formats has been a limiting factor for transportation agencies and practitioners involved in data analysis and reporting. A lack of common data formats negatively affects sharing data among partnering transportation agencies (multimodal transportation, planning, public safety, and emergency response agencies at the city, regional, and state levels); private-sector interests (e.g., transportation network companies, navigation providers, freight managers); travelers; and intelligent devices (e.g., traffic signals, ramp meters, connected vehicles). Well-designed data structures and processes can improve the efficiency of data-driven processes, and support innovation.
Some standards exist for data sharing within regional mobility management, but usually in specific segments of the operation (e.g., Traffic Management Data Dictionary [TMDD], Center to Center [C2C] protocols), which do not include all data elements needed for Integrated Corridor Management (ICM), and regional mobility management. For instance, TMDD works well for data sharing between traffic management centers, but does not include some data or granularity of data needed for decision support systems and modeling systems within ICM and smart city initiatives. For example, lane data (speed, volume, occupancy) is only available in most C2C systems at the macroscopic level (all lanes combined). Transit data within C2C systems is mostly static information and does not include real-time vehicle location and passenger count information.
Lessons learned from the ICM implementations, smart city programs, and regional mobility programs in the United States point to research gaps and ideas that can help data sharing programs. These gaps can be organized along three general areas: (a) data warehousing and data sharing standards, (b) use of Intelligent Transportation Systems (ITS) standards and regional ITS architectures, and (c) institutional coordination. NCHRP Scan Team Report “Advances in Strategies for Implementing Integrated Corridor Management (ICM) (NCHRP Project 20-68A, Scan 12-02) provides additional information on some of these issues.
As transportation agencies begin to use new data sources and to make their data more accessible, some of the issues faced include gaining confidence in the data and understanding their quality and variability, reconciling data discrepancies among different sources (including probe vehicles and connected vehicles), determining appropriate data sample sizes, validating processes for turning data into information, comparing system performance across time taking into account how the data has changed, and understanding the implications of big data and machine learning approaches.
NCHRP Project 08-36/Task 129 examined the feasibility of developing data standards for transportation planning and traffic operations. The contractor's report revealed that it is difficult to predict standard adoption; many well-designed and technically superior standards have failed. Based on the research, a business case and clear incentives for a critical mass of supportive stakeholders is required for market adoption. Standards are most successful if they have a clear business purpose; are clear in application, specificity, and versioning; are developed with broad outreach and buy-in; are well defined and simple; are open standards; are forward looking; and involve a national or worldwide community. The NCHRP Project 08-36/Task 129 contractor's report concluded that standards are feasible and desirable and identified five promising data areas or “bundles”: travel time, demand, traffic incident and work zones, network, and transit. Based partly upon this effort, the AASHTO Data Management and Analytics Committee has set a goal of developing a “Data ‘Green Book’” and this project is an initial step toward that goal.
