Spatiotemporal event sequences represent the sequences of event types whose spatiotemporal instances frequently follow each other in spatiotemporal context. In this work, we present spatiotemporal event sequence mining from spatio-temporal event datasets that contains evolving region trajectories. We propose two algorithms for discovering spatio-temporal event sequences. We formally define a flexible spatiotemporal follow relationship, introduce various data models for capturing the sequence forming behavior. Lastly, we present an extended experimental evaluation that demonstrates the computational efficiency of our algorithms.
This paper focuses on the study of detecting real-time pedestrian flow by using Bluetooth on smartphones to show evacuation route. When a natural disaster strikes, knowing real-time accurate pedestrian density and flow in wide range is very important to lead people to the safe places. Evacuation route can be calculated by agent-based simulation and the real-time pedestrian flow. One promising way to detect real-time pedestrian density and flow is mobile sensing. With devices the evacuee have, the pedestrian density of near area can be detected. In this paper, we introduce the method of detecting pedestrian density and propose how to apply this method to a disaster for solving crowded situation.
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.
Citizens in various locations can report local infrastructure issues to the government by posting reports on certain websites, such as Chiba Report and FixMyStreet. Recently, these systems have begun operating worldwide. In these systems, a large volume of information is collected on infrastructure problems that are identified by citizens (e.g., broken paving slabs, fly tipping, graffiti, potholes). This information is expected to be utilized for infrastructure maintenance. However, local problems (especially road defection) identified by citizens are sometimes not deemed an urgent matter for road managers. This is because it is difficult for an average person to determine road damage status. Furthermore, non-critical reports may be a burden for local government because each report requires visual confirmation. We therefore propose a smartphone application based on a deep neural network that can determine road damage status using only photographs of the road. This application is based on a deep neural network model trained by citizen reports and road manager inspection results, which are gathered daily on a government server. The application updates the model parameters each time it launches and thereby becomes increasingly more intelligent and effective. The proposed system enables average citizens to easily determine road damage status using only a smartphone application. In addition, because not only expert road managers but also local government officials without expert knowledge can inspect the road, the proposed system can be useful for local governments that lack expert road managers.
This paper describes a new Network-constrained Moving objects indexing structure, which extends the state-of-the-art for this kind of data. The indexing structure we propose is called Temporally Enhanced Network-Constrained R-tree (TENC R-tree), which solves the shortcomings in other Network-Constrained access methods like the FNR-tree [7], MON-tree [1] and UTR-tree. These existing indexing methods are designed to store and retrieve the moving objects based on spatial features, followed by their temporal ones. They are generally not efficient when a query has only temporal constraints, or when a specific moving object id is also part of the query conditions. In such cases, existing methods have to scan the entire database to retrieve the result. Furthermore, the aforementioned methods are not efficient in processing Strict-path query, which is a query type that retrieves trajectories that follow all the edges in the queried path [10]. Our proposed TENC R-tree index allows good performance for almost all types of queries on moving objects in a constrained network, whether the constraints are spatial, temporal, or based on object id. Also, the TENC R-tree out-performs other access methods on the case of Path queries. Our experiments show the performance has been improved by 10 to 100 times for such queries.
