Big data, collected in the form of social media posts and mobile phone location tracking, have great potential to inform and manage the planning and operation of transit networks in developing countries. Data are widely available, but the challenge, as with developed countries, is figuring out how best to use it. A case study method was used to consider approaches in Nairobi, Kenya; Istanbul, Turkey; and Dhaka, Bangladesh. In Nairobi, GPS location data were collected to generate the first map of the complex Matatu transit network. In Istanbul, automated fare collection systems were processed to understand better the usage of a bus rapid transit system. In Dhaka, researchers were collecting GPS positioning data to manage the city bus networks. Residents of these developing cities were frequent users of online media, as in many cities in the developing countries. This study revealed that integration of online media with location-based data provided a big data scenario that had the potential for supporting transit operations while posing challenges to the management of data mobility. It is not realistic to apply a one-size-fits-all approach to any problem in the developing world, but together the case studies show that with the right approach, technical capacity in transitional cities has the potential to grow to support higher-level data processing and make more efficient and more sustainable policy decisions for crucial urban transit networks in developing countries.
Robustness Analysis of Urban Transit Networks Based on Bipartite Graph Model
