What should urban transportation systems be trying to achieve? Most government officials have one goal only, which is to reduce congestion. They rely on engineering models and passenger tallies to estimate the immediate effects of a policy change—without necessarily taking into account all the behavioral adjustments that might eventually occur. And so additional highway lanes quickly fill up again due to “induced demand.” The total number of rides per day may increase, for example, but who rides is hardly ever mentioned. What would a more comprehensive approach look like? Economists often find it useful to imagine an ideal social planner. Specifically, how would such a czar set public transportation prices, offerings, and fleet sizes so that—when everyone from commuters to ride-share companies make their own best decisions—the resulting trips and trip durations maximize society’s total welfare after netting out costs of all kinds including externalities due to environmental damage, etc.? Even if it seems like a tall order, economists who study industrial organization naturally think about transportation in terms of spatial equilibrium models like this. Under the leadership of Milena Almagro, the researchers have been particularly successful so far at compiling and combining remarkable datasets. These include cell phone records from entire metropolitan areas. Besides travel routes and durations, they are also inferring information about travelers’ home and work locations, income, and other demographic details as well as estimates of environmental impacts, equity considerations, and other externalities. Based on such models and datasets, the team will dive into four deep research questions. They aim to characterize: 1) Optimal mixes of transportation modes, pricing strategies, and service levels; 2) Where expanding public transportation makes sense and where it does not; 3) The potential role of public transport that is “on-demand” rather than scheduled; 4) Gains due to transportation policy coordination across geographical jurisdictions. All will require the analysis of counterfactual scenarios, cross-subsidies, and other methodological challenges that this team is ready to overcome.