Hidekazu Kasahara

Bio: Hidekazu Kasahara is an academic researcher from Kyoto University. The author has contributed to research in topics: Global Positioning System & TRIPS architecture. The author has an hindex of 3, co-authored 15 publications receiving 26 citations.

Detecting Deviations from Intended Routes Using Vehicular GPS Tracks

Abstract: This article proposes a method to find intersections at which cars tend to deviate from the optimal route based on global positioning system (GPS) tracking data under the assumption that such deviations indicate that car navigation systems (CNSs) and road signage are not readily available. If the intended route is known, deviations can be enumerated by comparing the intended route with the vehicle’s actual route as observed by a GPS; however, the intended route is unknown and can differ from the route suggested by a CNS. To identify intersections with high deviation rates without knowing intended routes, we exhaustively sampled subsequences from each vehicular GPS track, and detected deviations from the optimal route for the subsequences. Although the detected deviations are not always caused by driver confusion, accumulating such erroneous detection results would yield a meaningful difference in the number of accumulated deviations at each intersection. We applied the proposed method to 3,843 GPS tracks collected from visitor drivers in the city of Kyoto. Thresholding the estimated deviation rate yielded 39 intersections from 14,543 candidates. The results show a certain level of correlation between obtained deviations and rerouting locations from actual CNS data. We also found several intersections where faulty route suggestions are provided by CNSs.

Business Model of Mobile Service for Ensuring Students’ Safety Both in Disaster and Non-disaster Situations During School Trips

Abstract: We are developing a mobile service for ensuring students’ safety during the school trips that provides the current position and a history of movement during their trips for non-disaster situations and helps to escape in the event of a disaster. Since the Great East Japan Earthquake, teachers are now required to ensure the security of students during school trips. We developed the mobile service to solve this increasing social needs. During the development, we found some issues to be solved beside the technical problems. Namely, (1) monetization of the business, (2) limited sales channel, (3) anxiety about the treatment of personal information and (4) teachers’ negative tendency to new technical tools. In this paper, from the viewpoint of the business, our approach to realize the service is described.

Evacuation support and safety confirmation sharing in disaster situations for school trips by mobile information system

Abstract: The recent earthquake in Japan showed that tourists cannot access evacuation information and the families of tourists experienced problems when accessing safety information related to tourists. Given these problems, we consider two issues related to information provision in disaster situations. The first issue is the lack of evacuation information for tourists. The second issue is the difficulty of confirming the safety of tourists and sharing their safety information with relevant people, including the tourist’s family. The present study focuses on developing a tourism information system to solve these issues. We refer to this system as an Educational Trip Support System (ETSS). The research subject is a school trip, which is a representative type of group tour that occurs in Japan. The objectives of the ETSS are to help students to escape to an evacuation area rapidly by providing evacuation information and to share safety confirmations with relevant people during disaster situations. We assessed the effectiveness based on a field test in a disaster-simulated situation and quantitative surveys. The major contributions of this study include (1) a description of a mobile application system for confirming safety during school trips and sharing information with relevant people, (2) a method to facilitate the rapid evacuation of students that saves time and reduces their concerns about the situation, (3) detailed evaluations of the performance obtained using ETSS.

Transportation Mode Annotation of Tourist GPS Trajectories Under Environmental Constraints

Abstract: Tourist transportation usage analysis provides basic information for tourism policy making. With the technical advances of tracking devices, GPS-equipped smartphones sense the movement of tourists and generate extensive volumes of movement data detailing tourist trajectories. Many researchers study semantic annotation using machine learning. However, it is necessary for machine learning to label the data for training; this requirement is costly. It would be useful for GPS semantic annotation if labelling the substantial amounts of GPS data could be avoided. In this research, we propose a new, simple GPS semantic annotation method using environmental constraints without machine learning. We call this method Segment Expansion with Environmental Constraints (SEEC) and assume a tourist behaviour model in which tourists move by foot and public transportation in touristic destinations that include numerous locations of interest. SEEC inferred the transportation modes of the GPS trajectory data at a 90.4 % accuracy level in the experiment.

Fishing Spot Prediction by Sea Temperature Pattern Learning

Abstract: Determination of appropriate fishing spots is one of the most important activities in the fishing industry. Inspired by the approach followed by fishermen to determine fishing spots, this paper presents a new machine-learning method for uncovering oceanographic patterns related to good fishing spots. Our method uses a sea temperature map as the input, extracts sea temperature patterns from arbitrary points on the map, and evaluates whether the patterns correspond to good fishing spots by using two machine learning techniques; one-class support vector machine (SVM) and spectral clustering. We evaluated the efficiency of our method using fishery data on neon flying squid.

Business intelligence and big data in hospitality and tourism : A systematic literature review

Abstract: PurposeThis paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research ga ...

Mobile technologies and applications towards smart tourism - state of the art.

Abstract: The popularity of mobile technologies and applications is constantly growing and undoubtedly changing consumers’ and providers’ behavior. The purpose of this study is to provide a comprehensive systematic literature review of academic research related to mobile technologies and applications in smart tourism published between 2012 and June 2017.,Published peer-reviewed articles were gathered from the three largest and most popular online databases and search engines – EBSCO host, Science Direct and Google Scholar and ENTER conference proceedings. Based on a keyword-driven search and content analysis, 126 articles were determined to be relevant to this study.,Selected publications were analyzed in accordance with the proposed research questions and thematically classified into three main categories: consumer perspective, technological perspective and provider perspective. The findings contribute to a better overall understanding of recent research into mobile technologies and applications in smart tourism by presenting the main results, methods, trends and other insights of relevant publications.,Although the researchers used two databases, one search engine and ENTER conference proceedings to collect articles, there is the possibility that some studies connected to the topic were not included. The study did not include books, other conference proceedings, literature reviews, theses, business reports and other possibly relevant publications.,This study provides a systematic review of the most recent published academic research (2012-June 2017; also including “Online First” articles) on mobile technologies and applications in smart tourism. The results of this study provide an agenda for future research in tourism and hospitality industry by identifying major trends and developments in smart tourism.

Classifying Human Activity Patterns from Smartphone Collected GPS data: A Fuzzy Classification and Aggregation Approach

Abstract: Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone-measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone-collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g., up to 96% in activity identification) with use of this method. Two examples were provided in the appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.

Convolutional LSTM based transportation mode learning from raw GPS trajectories

Abstract: With the advancement of location acquisition technologies, a large amount of raw global positioning system (GPS) trajectory data is produced by many moving devices. Learning transportation modes from the GPS trajectory data is an important problem in the domain of trajectory data mining. Traditional supervised learning-based approaches rely heavily on data preprocessing and feature engineering, which require domain expertise and are time consuming. The authors propose a deep learning-based convolutional long short term memory (LSTM) model for transportation mode learning, in which the convolution neural network is first used to extract deep high-level features and then LSTM is used to learn the sequential patterns in the data that uses both GPS and weather features, thus making the full use of spatiotemporal operations. The authors have also analysed the impact of the geospatial region on human mobility. Experiments conducted on the Microsoft Geolife data set fused with the weather data set show that their model achieves the state-of-the-art results. The authors compare the performance of their model with the benchmark models, which shows the superiority of their model having 3% improvement in accuracy using only GPS features, and the accuracy is further improved by 4 and 7% on including the impact of geospatial region and weather attributes, respectively.

Efficiency, effectiveness, and satisfaction of responsive mobile tourism websites: a mobile usability study

Abstract: Considering the high penetration of internet-enabled smartphones, it is not surprising that DMOs feel the need to adapt their websites and services for mobile devices, although these adaptations are very cost intensive. Responsive web design (RWD) offers an efficient and practicable solution to address the plethora of different mobile devices with countless varying characteristics (scree-size, input, size, etc.). Moreover, the lack of evidence about the effects of websites employing RWD on mobile usability, as well as tourism information search behavior, raises questions both to practitioners and researchers. With this paper we investigate the efficiency, effectiveness and satisfaction when searching for and encountering tourism information on a smartphone on a responsive mobile tourism website compared to a mobile adaptive website. Through an experiment, 20 participants interacted with two representative websites and fulfilled specific information retrieval tasks. Effects between both websites could be derived, although differences were not consistently significant, and well-applied heuristics failed to measure user behavior systematically. Overall the responsively designed website performed better but failed to distinguish itself in terms of satisfaction and perceived usability.