Bus Routes Design and Optimization via Taxi Data Analytics

Public bus services are often planned in the context of urban planning. For a city with efficient and extensive network of public transportation system like Singapore, enhancing the existing coverage of bus service to meet the dynamic mobility needs of the population requires data mining approach. Specifically, frequent taxi rides between two locations at a period of time may suggest possible poor coverage of public transport service, if not lacking of the public transport service. In this paper, we describe a proof of concept effort to discover this weakness and its improvement in public transportation system via mining of taxi ride dataset. We cluster taxi rides dataset to determine some popular taxi rides in Singapore. From the clustered taxi rides, we filter and select only the clusters whose commuting via existing public transport are tortuous if not unreachable door-to-door. Based on the discovered travel pattern, we propose new bus routes that serve the passengers of these clusters. We formulate the bus planning problem as an optimization of directed cycle graph, and present it's preliminary solution and results. We showcase our idea in the case of Singapore.