We analyze the spatially distributed impacts of transport investment in urban highways and public transport with a novel methodology based on the capabilities of online technology to replicate the (unobserved) condition without highways. This is based upon the intensive use of Google Maps API (GMA) to obtain travel times between each origin-destination pair at a highly detailed level to reveal the effects of new infrastructure on different zones and groups within a city. Santiago is used as a case study, as the city introduced 150 km of urban highways, a reorganization of surface transit, and new subway lines in a relatively short period. We show that the high-income segment of the population has been the most favored, simultaneously increasing the difference between transit and car travel times in those areas where car ownership is low, stimulating the acquisition of a car.

In this paper, we incorporate the spacing of transit lines in addition to frequencies, vehicle sizes and routes in both the design and the analysis of scale economies in transit systems. First, we present a way of looking at lines spacing in a simple parallel-lines-model whose properties regarding optimal design and scale economies are derived. Then we introduce this concept of spacing into the parametric description of a city - that permits the representation of different degrees of mono and polycentrism - in order to analyze the choice between basic strategic lines structures as feeder-trunk, hub-and-spoke or direct services, where lines spacing is optimized jointly with frequencies, vehicle sizes and routes of all lines involved. We show that (a) there is a link between optimal spacing and frequency such that waiting and access costs are equal; (b) the inclusion of spacing increases the range of demand volumes where transit networks that include transfers are preferred; (c) the degrees of mono and polycentrism influences optimal spacing; and (d) introducing spacing increases the degree of scale economies.