Ko Ko Lwin

Shortages of engineers and financial resources have made it difficult for municipalities to identify and address problems with aging road infrastructures. To resolve these problems, numerous studies have focused on automating road inspection, including a study in which we developed a smartphone-based road inspection system. For efficient operation of the system, it is necessary to understand the usage of vehicles in which the system will be installed. In this study, we analyzed the usage of public vehicles with long-term global positioning system (GPS) probe data collected from public vehicles operating in Kakogawa city and Fujisawa city in Japan. As a result, we discovered that local governments of the same size have similar tendencies in terms of road coverage. Moreover, we found that installing road inspection systems on only a few public vehicles can cover the entire road inspection area. We anticipate that these results will assist local governments in making informed decisions during the system introduction process and provide an indicator of the accuracy required for road inspection systems to future researchers.

The rise in big data applications in urban planning and transport management is now widening and becoming a part of local government decision-making processes. Understanding people flow inside the city helps urban and transport planners build a healthy and lively city. Many flow maps are based on origin-and-destination points with crossing lines, which reduce the map’s readability and overall appearance. Today, with the emergence of geolocation-enabled handheld devices with wireless communication and networking capabilities, human mobility and the resulting events can be captured and stored as text-based geospatial big data. In this paper, we used one-week mobile-call-detail records (CDR) and a GIS road network model to estimate hourly link population and flow directions, based on mobile-call activities of origin–destination pairs with a shortest-path analysis for the whole city. Moreover, to gain the actual population size from the number of mobile-call users, we introduced a home-based magnification factor (h-MF) by integrating with the national census. Therefore, the final output link data have both magnitude (actual population) and flow direction at one-hour intervals between 06:00 and 21:00. The hourly link population and flow direction dataset are intended to optimize bus routes, solve traffic congestion problems, and enhance disaster and emergency preparedness.