Waze, Twitter, variable message signs, Google Transit, transportation agency live camera feeds, and scores of mobile apps and other sources provide high quality, dynamic information to travelers in cities around the country. This information, much of it crowdsourced, epitomizes the allure of big data and smart and connected communities. Readily available to almost any traveler with a smart phone, it holds the promise of enabling better decisions to reduce traffic congestion, improve road safety, facilitate transit use, guide infrastructure investments, and otherwise improve transport outcomes. However, two stumbling blocks cloud this vision. These relate to, first, the challenges digital overload and other human cognitive limitations impose on decision-making; and, second, the reluctance of many transportation agencies to rely heavily on crowdsourced data in their operational (e.g., traffic management) and longer-term (e.g., infrastructure planning) decisions.

This planning project addresses these two challenges. More specifically, PIs are working with public transportation managers in two inner-ring, Washington, DC metro area counties with highly diverse national- and foreign-born populations to develop a longer-term research effort to understand: (1) personal and institutional factors that influence the generation and use of information from crowdsourcing apps and other digital technologies in transportation; (2) impacts of perceived information overload on drivers, public managers, and planners (consumers and producers of information); 3) effect of overload on transportation incidents and patterns, and 4) effects on transport system performance of different types, levels, and quality of crowd-sourced transport information. Ultimately, PIs seek to yield better transportation outcomes for travelers in our study area, and to provide transferable lessons for communities around the country.

The work integrates across social psychology, public administration, decision sciences, transportation engineering, and computer sciences. Its focus on digital overload, co-production of information by individuals and institutions, and model-based informatics uniquely captures decision dynamics widely distributed across space and individual actors. As such, it seeks to advance understanding of smart transportation system performance by incorporating a neglected element of information use (overload) critical to traveler behavior. By expanding a model-based informatics perspective to consider overload and aggregating decision-making distributed across a large number of individuals, it augments social psychology efforts to capture the collective effects of individual overload and stress as well. It also fosters new synergies between public administration -- which rarely considers uncertainty and risk as central parts of a decision situation -- and the behavioral decision sciences, which rarely consider the public interest nature of managers' responsibilities and their collaborative decision environment.