Akinori Asahara

Wireless LAN based indoor positioning using radio-signal strength distribution modeling

Aiming to solve one of the serious problems of radio-signal strength (RSS) indoor positioning (namely, fluctuation of RSS values due to reflective waves), a novel indoor-positioning method based on RSS distribution modeling (called “RSS distribution modeling using the mirror-image method,” RDMMI), was developed and evaluated. With RDMMI, a model of RSS distribution is created by using measured RSS and considering reflective waves. RDMMI achieves an average positioning error of 3.1 m, which exceeds that of conventional nearest-neighbor (NN) method by 0.6 m. It also accomplishes average positioning error of 3.7 m with a small set of randomly chosen RSS measurements. Furthermore, even in the case that radio source positions are unknown, RDMMI exceeds NN method.

Spatio-temporal similarity search method for disaster estimation

For fast disaster estimation after a large-scale disaster occurs, this paper presents a fast spatio-temporal similarity search method that searches a database storing many scenarios of disaster simulation results represented by time-series grid data for some scenarios similar to insufficient observed data sent from sensors. The proposed method efficiently processes spatio-temporal intersection by using a spatiotemporal index to reduce the processing time for the spatiotemporal similarity search. Additionally, this paper presents the efficient spatio-temporal range search method by using this spatio-temporal index. The spatio-temporal range search is needed for the analysis and visualization in order to grasp a damage situation after spatio-temporal similarity search returns some scenarios similar to observed data. The results of the performance evaluation show that the proposed method has a shorter response time for the spatiotemporal similarity search than two conventional methods that use a temporal index and a spatial index. They also show that the response time is within about 30 seconds when the proposed method searches the database storing 50 billion time-series grid data items for some scenarios similar to 100 observed data items. As a result, the proposed method can be applied to a real environment in which a spatio-temporal similarity search needs to processed within 10 minutes. Additionally, the evaluation results show that the spatio-temporal range search method by using the spatio-temporal index can be also applied to a real environment.

Locally Differential Map Update Method with Maintained Road Connections for Telematics Services

Map databases for car navigation systems loaded with hard disk drives are rewritable. Thus, the ability to update map databases in such systems is expected. Furthermore, because telematics services are available through cell phones with reasonable costs, it is also becoming possible to download new information into car navigation systems anywhere. Hence, drivers want to update map databases in their terminals through telematics services. However, the volume of the entire map database is too large to be distributed through cell phones. Therefore, only changed data of the map database are distributed in differential map updates. However, even considering the reduction, the data size is still too large to distribute through cell phones. A Locally Differential Map Update (LDMU) is effective to reduce the data size, because with this method, a map is only partially updated. However, LDMU causes disconnection of the road network. Therefore, we propose Connection Maintained Locally Differential Map Update (CM-LDMU). This involves partially updating a map while maintaining the road network. In experiments with this method, less than 250 KB of data was required to update 20 times 20 km2 as wide as a medium-sized city. Thus, the feasibility of telematics map updating services is demonstrated.

Macroscopic Structural Summarization of Road Networks for Mobile Traffic Information Services

The need for traffic information service systems that warn about traffic congestion and accidents through mobile devices, such as cellular phones, is increasing greatly now. To display clearly these informations for these services, a well-formed map is more useful than a detailed one in many cases. Although methods to generate well-formed map automatically are known as map summarization methods, summarization of road maps that cover wide areas has not been studied sufficiently. Therefore, we developed a new algorithm that summarizes the network structure of a detailed road network into a simple macroscopic structure. Further, we evaluated this algorithm’s effectiveness by experiments with 40 samples of road routes from the main road networks in Japan. The proposed method reduced the link number by 41.3% and reduced the fractal indices by 62.6%, and this proved simplification. And the proposed method improved the well-formed map by user evaluation. From these results obtained by our evaluation, we found that our method is effective for constructing a system for providing practical mobile traffic information services.

Real-time people movement estimation in large disasters from several kinds of mobile phone data

Recently, an understanding of mass movement in urban areas immediately after large disasters, such as the Great East Japan Earthquake (GEJE), has been needed. In particular, mobile phone data is available as time-varying data. However, much more detailed movement that is based on network flow instead of aggregated data is needed for appropriate rescue on a real-time basis. Hence, our research aims to estimate real-time human movement during large disasters from several kinds of mobile phone data. In this paper, we simulate the movement of people in the Tokyo metropolitan area in a large disaster situation and obtain several kinds of fragmentary movement observation data from mobile phones. Our approach is to use data assimilation techniques combining with simulation of population movement and observation data. The experimental results confirm that the improvement in accuracy depends on the observation data quality using sensitivity analysis and data processing speed to satisfy each condition for real-time estimation.

Evaluation of Trajectory Clustering Based on Information Criteria for Human Activity Analysis

In this paper, we discuss statistical analysis of human trajectories measured by GPS-like positioning devices. Our goal is to develop a system of trajectory analysis that distributes information optimized for each user. For such a system, we need a method to estimate a users status from his/her trajectories. First, a trajectory needs to be divided into short temporal segments, which will be matched to action model patterns, to estimate a users status. Second, we tried dividing actual human trajectories using a conventional trajectory-clustering method. Moreover, we adjusted parameters of the trajectory clustering by using information criteria experimentally. After the experiment, we confirmed that only a criterion in which noise data are counted worked well. However, we also confirmed that the number of clusters generated by the method is too small. Therefore, we conclude that an improvement in deciding which data are noise in trajectory clustering is necessary for estimating the status of users.

Decomposition of pedestrian flow heatmap obtained with monitor-based tracking

Indoor pedestrian tracks are now obtained with monitoring systems, such as video and LiDAR. Statistics using the track information, such as the number of pedestrians and average moving speeds, gives facility managers insights into how to improve the floor plan. However, the statistics sometimes convolute multiple modes of pedestrian movement trends such as “most pedestrians are going straight”. To address the problem, decomposition of the pedestrian-track statistics is proposed in this work. The proposed method is based on a mixed Markov-chain model for rough transition between areas. Decomposed statistics are calculated after classifying tracks with the model into several groups of pedestrians. Moreover, statistics decomposed with the proposed method and existing methods are experimentally demonstrated with actual pedestrian tracks in this paper.

Quantity Distribution Search using Sparse Representation Generated with Kernel-based Regression.

The number of records representing a quantity distribution (e.g. temperature and rainfall) requires an extreme amount of overhead to manage the data. We propose a method using a subset of records against the problem. The proposed method involves an approximation derived with kernel ridge regression in advance to determine the minimal dataset to be input into database systems. As an advantage of the proposed method, processes to reconstruct the original dataset can be completely implemented with Structured Query Language, which is used for relational database systems. Thus users can analyze easily the quantity distribution. From the results of experiments using digitized elevation map data, we confirmed that the proposed method can reduce the number of data to less than 1/10 of the original number if the acceptable error was set to 125 m.

Composition of simulation data for large-scale disaster estimation

When a large-scale natural disaster occurs, it is necessary to quickly collect damage information so that disaster-relief operations and wide-area support in accordance with the scale of the natural disaster can be initiated. Previously, we proposed a fast spatio-temporal similarity search method (called the STSim method) that searches a database storing many scenarios of disaster simulation data represented by time-series grid data for scenarios similar to insufficient observed data sent from sensors. When the STSim method is naively applied for estimating disasters occurring at multiple locations, e.g., fire spreading after a large-scale earthquake, it must prepare a huge number of combinations consisting of scenarios that represent disasters at multiple locations. This paper presents a combination method of simulation data in order to apply the STSim method for estimating disasters occurring at multiple locations. This proposed method stores scenarios, each of which represents a disaster occurring at a single location, to a database; thus, reducing the number of scenarios. After a disaster occurs, it extracts and composes scenarios similar to observed data, resulting in efficient disaster estimation in any situation. We conducted performance evaluations under the assumption that an earthquake occurs below the Tokyo metropolitan region and estimating the spread of fire in the initial response. These results of the processing time for estimating the spread of fire show that the processing time is within 10 minutes, which is practical.

Pedestrian-Flow Analysis System for Improving Layout of Exhibitions

A system for practical pedestrian-track analysis at an actual exhibition is demonstrated. Track data obtained at the exhibition was uploaded to a spatio-temporal database, and the key features of the technical exhibition were determined. New knowledge derived from these features was successfully applied to improve the layout of the next event.