Taxi cab service optimization using spatio-temporal implementation to hot-spot analysis with taxi trajectories: a case study in Seoul, Korea

Currently there are demands for maximization of taxi services and also for saving fuel usage within massive cities. Spatial big data extracted from taxi service records and GPS can be used to suggest optimal routing options to achieve these goals. The taxi cab ride data contains 7,000 unique taxies being serviced in Seoul, South Korea. In this study one week worth of data with the size of 3.13GB were used. Also road network data provided by Ministry of Land, Infrastructure and Transport (MOLIT), which contains 19,229 nodes and 22,192 links, and census map provided by Statistics Korea were used as base-map. Lastly floating population data of Seoul city area, gathered with mobile phones, has been used as an index of demand for taxi service. By using taxi cab ride data, which contains trajectory with time and 2D coordinates, and information about whether passenger is on the taxi or not, hot spots were analyzed for 1) taxies without passengers whom are available to pick-up passengers, 2) places where people are experiencing difficulty hailing a taxi due to high demand for taxi. Combination of these two types of hot spots can provide new insight for both public and commercial sectors to maximize the efficiency of taxi service and to reduce idle fuel usage. Afterwards the floating population data is used to provide indices for taxi usage in Seoul area, providing further insights. Utilizing the time stamp records on the taxi GPS data, hourly based hot spots for both 'demand' and 'supply' for taxi cab ride can be derived, and this outcome can be practically used to guide taxi drivers to high demanding places and avoid high supplying places.