City buses are important transportation for citizens who especially living in transportation vulnerable areas. However, public transportation in these areas continuously decreases because of low population density. The low demand puts these areas behind service priorities, The backlog of service priorities exacerbates public transportation, hereby decreasing the population in these areas.In order to solve the deterioration of public transportation services in transportation vulnerable areas, the optimal route is calculated through the genetic algorithm-based approaches. The methodology automatically creates novel bus routes based on the demands and existing transportation resources. In this paper, each route is regarded as a living organism, and both demand networks and road networks are constructed as an evolutionary environment so that each organism can compete and evolve with each other. In order to verify the methodology, the case study is conducted based on actual data-sets from local government, bus companies, telecommunication company(SK Telecom) in Incheon, Republic of Korea. The second largest island (Yeongjongdo) in Incheon is selected as a transportation vulnerable area by the Incheon institute (public research center for Incheon metropolitan city). The optimal route from the methodology increases the route efficiency without supplementing additional buses to handle the demands, hereby improving the vicious cycle of transportation vulnerable areas.

Computer sciences and related disciplines evolve around developing, evaluating, and applying algorithms. Typically, an algorithm is not developed from scratch, but uses and builds upon existing ones, which often are proposed and published in scholarly articles. The ability to capture this evolution relationship among these algorithms in scientific literature would not only allow us to understand how a particular algorithm is composed, but also shed light on large-scale analysis of algorithmic evolution through different temporal spans and thematic scales. We propose to capture such evolution relationship between two algorithms by investigating the knowledge represented in citation contexts, where authors explain how cited algorithms are used in their works. A set of heterogeneous ensemble machine-learning methods is proposed, where the combination of two base classifiers trained with heterogeneous feature types is used to automatically identify the algorithm usage relationship. The proposed heterogeneous ensemble methods achieve the best average F1 of 0.749 and 0.905 for fine-grained and binary algorithm citation function classification, respectively. The success of this study will allow us to generate a large-scale algorithm citation network from a collection of scholarly documents representing multiple time spans, venues, and fields of study. Such a network will be used as an instrument not only to answer critical questions in algorithm search, such as identifying the most influential and generalizable algorithms, but also to study the evolution of algorithmic development and trends over time.