The majority of transit trip planners exist as proprietary systems based on particular vendor products. With the incorporation of more functional components, system maintenance and regular transit information updates become burdensome tasks for transit agencies. In addition, the proprietary nature of the systems makes it difficult to take advantage of the rapid advancement of geospatial information and web technologies. The authors proposed an open and interoperable transit trip-planning system based on a service-oriented architecture, with the principle of reusing the existing modular resources, while providing user-friendly interfaces for expansion of functionality. The objective was to integrate geospatial services available online (such as Google Maps), open-source geospatial database technologies, and path-finding algorithms in a loosely coupled manner. The proposed system was developed with spatial and temporal transit data from Waukesha Metro Transit in Wisconsin. Research results were validated by comparing outputs from the existing South-East Wisconsin Transit Trip Planner and route schedule matching. Comparison results showed that the new service-oriented architecture provided a flexible, efficient mechanism for transit-trip planners. The architecture took advantage of rapidly changing online geospatial services, yet maintained the core functions of itinerary search that may be unique to each transit agency.
With the rapid development of urban road traffic, there are a certain number of black spots in an urban road network. Therefore, it is important to create a method to effectively identify the urban road black spots in order to quickly and accurately ensure the safety of residents and maintain the sustainable development of a city. In this study, a GIS (geographic information system) and the Firefly Clustering Algorithm are combined. On the one hand, a GIS can accurately extract the distance between accident points through its spatial analysis function, overcoming the disadvantage of the accident data not usually including the specific location data. On the other hand, the Firefly Clustering Algorithm can be used to comprehensively extract the characteristics of accident points, which is particularly suitable for the identification of black spots. In order to verify the feasibility of the proposed method, this research compares the identification effect between the OD (origin–destination) cost distance calculated by GIS and the Euclidean distance. The results show that the Euclidean distance is smaller than the OD cost distance and that the accident search method based on the Euclidean distance can overestimate the number of black spots, especially for intersections. Therefore, the proposed method based on the Firefly Clustering Algorithm and GIS can not only contribute to identifying urban road black spots but also plays an auxiliary role in reducing urban road crashes and maintaining sustainable urban development.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.