Traffic congestion increases travel time and is a major source of pollution and health damage in developing-country cities. Data scarcity frequently confines traffic improvement projects to sites where congestion can be easily measured. This article uses spatiotemporal data from new global sources to revisit the siting problem in Dhaka, Bangladesh, where local congestion measures are augmented by estimates of citywide travel time, pollution exposure, and pollution vulnerability. We combine Google Traffic data with an econometric model linking traffic, pollution readings from a local monitoring station, and weather data to estimate the spatial distribution of vehicular pollution. We explore pollution-vulnerability implications by incorporating spatial distributions of poor households, children, and the elderly. Using the Open Source Routing Machine and OpenStreetMaps, we estimate systemwide travel-time gains from reducing congestion at each point in a grid covering the Dhaka metro area. We find a large divergence of siting priorities in single-dimensional exercises that focus exclusively on local congestion, citywide travel time, vehicular pollution, or vulnerable-resident pollution exposure. By implication, optimal siting requires a social objective function with explicit weights assigned to each of the four dimensions. The new global information sources permit extending this multidimensional approach to many cities throughout the developing world.