Research

Support for massive investments in transportation infrastructure, possibly with a change in the share of spending on transit, seems widespread. Such proposals are often motivated by the belief that our infrastructure is crumbling, that infrastructure causes economic growth, that current funding regimes disadvantage rural drivers at the expense of urban public transit, or that capacity expansions will reduce congestion. In fact, most US transportation infrastructure is not deteriorating and the existing scientific literature does not show that infrastructure creates growth or reduces congestion. However, current annual expenditure on public transit buses exceeds that on interstate construction and maintenance. A careful examination of how funding is allocated across modes is suggested by the evidence. Massive new expenditures are not.

This chapter provides a practical, hands-on guide to evaluating large transport infrastructure investments, including national roads, public transit, and bus rapid transit corridors, using a geospatial impact evaluation (GIE) approach. Targeted at evaluators, policy analysts, and applied researchers, it demonstrates how spatially explicit data can be combined with quasi-experimental methods to estimate causal impacts where infrastructure placement is non-random and benefits extend beyond immediate locations. Geospatial data support both measurement and identification in transport evaluations. Using remote-sensing products and GIS network layers, the chapter shows how spatial datasets enable long-term assessment of economic, land-use, and environmental outcomes. Key inputs include nighttime lights as proxies for economic activity, land cover and land-use maps, high-resolution population grids, and road networks to quantify accessibility. These data overcome survey limitations by providing continuous coverage over multiple periods. Three quasi-experimental methods are implemented: (1) difference-in-differences exploiting staggered roll-outs; (2) instrumental variables using least-cost network paths; and (3) market-access models linking travel time reductions to economic opportunity. Case studies in Lagos, Nigeria, and Ethiopia illustrate localized and spatially diffuse impacts, equipping readers to conduct transparent, reproducible, policy-relevant GIE.