Integrating Real-time Bus-Tracking with Pedestrian Navigation in a Journey Planning System

Automated Vehicle Location (AVL) systems provide real-time location information for emergency response, delivery services and freight transport. The advent of AVL systems has meant both public and private bus operators can implement systems to provide real-time passenger information, analyse their service performance and also to evaluate the quality of their operations. Traffic congestion, intersection delays, weather and operational conditions are some of the factors that make it difficult to predict the accurate bus arrival time in a real-time environment. In a joint project between NUI Maynooth and Blackpool Transport, a dynamic web application was developed to display and update vehicle locations (bustracking.co.uk) (Winstanley et al. 2009) and to provide predictive bus arrival times at stops. A journey by bus is usually part of a longer door-to-door itinerary, usually involving walking before, after or between bus segments. The passenger is really interested in door-to-door journey times when making decisions about time of departure and which bus to catch. Therefore journey planners that combine the pedestrian and bus journeys are required and indeed several such systems exist, such as Transport Direct (2009), Traveline Midlands (2009), Google transit (2009). However these systems are mainly designed to plan journeys in advance and so base their decisions on the fixed bus timetable. For last-minute planning, and also for updating journey plans as-you-go, real-time bus locations and short-term predictions of bus arrival times at stops can be used to give more reliable journey times taking into account delays due to congestion, diversions and other factors. This paper describes an experimental system that combines bus tracking and pedestrian navigation

Development of a server to manage a customised local version of OpenStreetMap in Ireland

In this paper we describe the software architecture of a prototype web-based GIS system for the deliver environmental research data in Ireland. The central component in this system is OpenStreetMap which provides the base layers of geographical data. An OpenStreetMap data collection campaign for our university town was carried out earlier this year yieldeding a spatially rich OpenStreetMap representation of Maynooth. Our server (OpenStreetMap database, supporting software, and specially generated map tiles) has been used by several GIS and location-based services projects in our department. One such example is a mobile device-based pedestrian navigation system is described in this paper. We describe some of the components of our server system. This includes a description of the management of the local copy of the OpenStreetMap database and the generation of sets of customised map tiles

Using a fully open source approach to working with OpenStreetMap

OpenStreetMap is a very exciting and vibrant project aiming to make accessing geographic data easier. Our research group at the Department of Computer Science NUI Maynooth Ireland is carrying out research into a broad range of topics including: map data generalisation, geographic shape complexity, web map services, map-based interface for pedestrian navigation. One of the common themes across this research is the use of OpenStreetMap as the principal source of geospatial data. In this paper we describe how our research productivity, research collaboration, and general data interoperability have been greatly enhanced from our early adoption of a fully open source GIS approach to working with OpenStreetMap. While one can work successfully with OpenStreetMap in non-Open Source environments the flexibility offered by an open source approach is a major advantage. This flexibility is delivered in many flavours including: a wide choice of software, inter-connectability of software packages and components, a wide network of support through documentation, message boards, and free and open exchange of ideas

Using a fully open source approach to working with OpenStreetMap

OpenStreetMap is a very exciting and vibrant project aiming to make accessing geographic data easier. Our research group at the Department of Computer Science NUI Maynooth Ireland is carrying out research into a broad range of topics including: map data generalisation, geographic shape complexity, web map services, map-based interface for pedestrian navigation. One of the common themes across this research is the use of OpenStreetMap as the principal source of geospatial data. In this paper we describe how our research productivity, research collaboration, and general data interoperability have been greatly enhanced from our early adoption of a fully open source GIS approach to working with OpenStreetMap. While one can work successfully with OpenStreetMap in non-Open Source environments the flexibility offered by an open source approach is a major advantage. This flexibility is delivered in many flavours including: a wide choice of software, inter-connectability of software packages and components, a wide network of support through documentation, message boards, and free and open exchange of ideas

Comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps

We describe a comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps. Five case study cities and towns are chosen for this comparison. Each mapping system is analysed for accuracy under three main headings: spatial coverage, currency, and ground-truth positional accuracy. We find that while there is no clear winner amongst the three mapping platforms each show individual differences and similarities for each of the case study locations. We believe the results described in this paper are useful for those developing Location-based services for countries such as Ireland where access to high-quality geospatial data is often prohibitively expensive or made difficult by other barriers such as lack of data or access restrictions

Comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps

We describe a comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps. Five case study cities and towns are chosen for this comparison. Each mapping system is analysed for accuracy under three main headings: spatial coverage, currency, and ground-truth positional accuracy. We find that while there is no clear winner amongst the three mapping platforms each show individual differences and similarities for each of the case study locations. We believe the results described in this paper are useful for those developing Location-based services for countries such as Ireland where access to high-quality geospatial data is often prohibitively expensive or made difficult by other barriers such as lack of data or access restrictions

3D oceanographic data compression using 3D-ODETLAP

This paper describes a 3D environmental data compression technique for oceanographic datasets. With proper point selection, our method approximates uncompressed marine data using an over-determined system of linear equations based on, but essentially different from, the Laplacian partial differential equation. Then this approximation is refined via an error metric. These two steps work alternatively until a predefined satisfying approximation is found. Using several different datasets and metrics, we demonstrate that our method has an excellent compression ratio. To further evaluate our method, we compare it with 3D-SPIHT. 3D-ODETLAP averages 20% better compression than 3D-SPIHT on our eight test datasets, from World Ocean Atlas 2005. Our method provides up to approximately six times better compression on datasets with relatively small variance. Meanwhile, with the same approximate mean error, we demonstrate a significantly smaller maximum error compared to 3D-SPIHT and provide a feature to keep the maximum error under a user-defined limit

BITOUR: A Business Intelligence Platform for Tourism Analysis

[EN] Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination.This work has been supported by COLCIENCIAS through a PhD scholarship. This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Bustamante, A.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2020). BITOUR: A Business Intelligence Platform for Tourism Analysis. ISPRS International Journal of Geo-Information. 9(11):1-23. https://doi.org/10.3390/ijgi9110671S123911Nakahira, K. T., Akahane, M., & Fukami, Y. (2012). The Difference and Limitation of Cognition for Piano Playing Skill with Difference Educational Design. Smart Innovation, Systems and Technologies, 609-617. doi:10.1007/978-3-642-29934-6_59Chua, A., Servillo, L., Marcheggiani, E., & Moere, A. V. (2016). Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy. Tourism Management, 57, 295-310. doi:10.1016/j.tourman.2016.06.013Karagiannakis, N., Giannopoulos, G., Skoutas, D., & Athanasiou, S. (2015). OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap. Proceedings of the 9th ACM Conference on Recommender Systems. doi:10.1145/2792838.2796555Burcher, M., & Whelan, C. (2017). Social network analysis as a tool for criminal intelligence: understanding its potential from the perspectives of intelligence analysts. Trends in Organized Crime, 21(3), 278-294. doi:10.1007/s12117-017-9313-8Alcabnani, S., Oubezza, M., & Elkafi, J. (2019). An approach for the implementation of semantic Big Data Analytics in the Social Business Intelligence process on distributed environments (Cloud computing). Proceedings of the 4th International Conference on Big Data and Internet of Things. doi:10.1145/3372938.3373003Zeng, B., & Gerritsen, R. (2014). What do we know about social media in tourism? A review. Tourism Management Perspectives, 10, 27-36. doi:10.1016/j.tmp.2014.01.001Lalicic, L. (2018). Open innovation platforms in tourism: how do stakeholders engage and reach consensus? International Journal of Contemporary Hospitality Management, 30(6), 2517-2536. doi:10.1108/ijchm-04-2016-0233Dwyer, L., & Kim, C. (2003). Destination Competitiveness: Determinants and Indicators. Current Issues in Tourism, 6(5), 369-414. doi:10.1080/13683500308667962Gomezelj, D. O., & Mihalič, T. (2008). Destination competitiveness—Applying different models, the case of Slovenia. Tourism Management, 29(2), 294-307. doi:10.1016/j.tourman.2007.03.009Zhong, L., Deng, J., & Xiang, B. (2008). Tourism development and the tourism area life-cycle model: A case study of Zhangjiajie National Forest Park, China. Tourism Management, 29(5), 841-856. doi:10.1016/j.tourman.2007.10.002Fernández, J. I. P., & Rivero, M. S. (2009). Measuring Tourism Sustainability: Proposal for a Composite Index. Tourism Economics, 15(2), 277-296. doi:10.5367/000000009788254377Cibinskiene, A., & Snieskiene, G. (2015). Evaluation of City Tourism Competitiveness. Procedia - Social and Behavioral Sciences, 213, 105-110. doi:10.1016/j.sbspro.2015.11.411Business Intelligence (BI)—Glossaryhttps://www.gartner.com/it-glossary/business-intelligence-bi/Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 30(12), 3514-3554. doi:10.1108/ijchm-07-2017-0461Maeda, T. N., Yoshida, M., Toriumi, F., & Ohashi, H. (2016). Decision Tree Analysis of Tourists’ Preferences Regarding Tourist Attractions Using Geotag Data from Social Media. Proceedings of the Second International Conference on IoT in Urban Space. doi:10.1145/2962735.2962745Guy, I., Mejer, A., Nus, A., & Raiber, F. (2017). Extracting and Ranking Travel Tips from User-Generated Reviews. Proceedings of the 26th International Conference on World Wide Web. doi:10.1145/3038912.3052632Peng, M. Y.-P., Tuan, S.-H., & Liu, F.-C. (2017). Establishment of Business Intelligence and Big Data Analysis for Higher Education. Proceedings of the International Conference on Business and Information Management - ICBIM 2017. doi:10.1145/3134271.3134296Castellanos, M., Gupta, C., Wang, S., Dayal, U., & Durazo, M. (2012). A platform for situational awareness in operational BI. Decision Support Systems, 52(4), 869-883. doi:10.1016/j.dss.2011.11.011Cohen, L. (2017). Impacts of business intelligence on population health. Proceedings of the South African Institute of Computer Scientists and Information Technologists on - SAICSIT ’17. doi:10.1145/3129416.3129441Love, M., Boisvert, C., Uruchurtu, E., & Ibbotson, I. (2016). Nifty with Data. Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education. doi:10.1145/2899415.2899431Berndt, D. J., Hevner, A. R., & Studnicki, J. (s. f.). Hospital discharge transactions: a data warehouse component. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. doi:10.1109/hicss.2000.926791Musa, G. J., Chiang, P.-H., Sylk, T., Bavley, R., Keating, W., Lakew, B., … Hoven, C. W. (2013). Use of GIS Mapping as a Public Health Tool–-From Cholera to Cancer. Health Services Insights, 6, HSI.S10471. doi:10.4137/hsi.s10471Mooney, S. J., Westreich, D. J., & El-Sayed, A. M. (2015). Commentary. Epidemiology, 26(3), 390-394. doi:10.1097/ede.0000000000000274Wisniewski, M. F., Kieszkowski, P., Zagorski, B. M., Trick, W. E., Sommers, M., & Weinstein, R. A. (2003). Development of a Clinical Data Warehouse for Hospital Infection Control. Journal of the American Medical Informatics Association, 10(5), 454-462. doi:10.1197/jamia.m1299Miah, S. J., Vu, H. Q., Gammack, J., & McGrath, M. (2017). A Big Data Analytics Method for Tourist Behaviour Analysis. Information & Management, 54(6), 771-785. doi:10.1016/j.im.2016.11.011Li, D., Deng, L., & Cai, Z. (2019). Statistical analysis of tourist flow in tourist spots based on big data platform and DA-HKRVM algorithms. Personal and Ubiquitous Computing, 24(1), 87-101. doi:10.1007/s00779-019-01341-xKrawczyk, M., & Xiang, Z. (2015). Perceptual mapping of hotel brands using online reviews: a text analytics approach. Information Technology & Tourism, 16(1), 23-43. doi:10.1007/s40558-015-0033-0Alaei, A. R., Becken, S., & Stantic, B. (2017). Sentiment Analysis in Tourism: Capitalizing on Big Data. Journal of Travel Research, 58(2), 175-191. doi:10.1177/0047287517747753Thelwall, M. (2019). Sentiment Analysis for Tourism. Big Data and Innovation in Tourism, Travel, and Hospitality, 87-104. doi:10.1007/978-981-13-6339-9_6Höpken, W., Fuchs, M., Höll, G., Keil, D., & Lexhagen, M. (2013). Multi-Dimensional Data Modelling for a Tourism Destination Data Warehouse. Information and Communication Technologies in Tourism 2013, 157-169. doi:10.1007/978-3-642-36309-2_14Sabou, M., Onder, I., Brasoveanu, A. M. P., & Scharl, A. (2015). Towards Cross-Domain Decision Making in Tourism: A Linked Data Based Approach. SSRN Electronic Journal. doi:10.2139/ssrn.2580242Fermoso, A. M., Mateos, M., Beato, M. E., & Berjón, R. (2015). Open linked data and mobile devices as e-tourism tools. A practical approach to collaborative e-learning. Computers in Human Behavior, 51, 618-626. doi:10.1016/j.chb.2015.02.032Wöber, K. W. (2003). Information supply in tourism management by marketing decision support systems. Tourism Management, 24(3), 241-255. doi:10.1016/s0261-5177(02)00071-7Vajirakachorn, T., & Chongwatpol, J. (2017). Application of business intelligence in the tourism industry: A case study of a local food festival in Thailand. Tourism Management Perspectives, 23, 75-86. doi:10.1016/j.tmp.2017.05.003Diakopoulos, N., Naaman, M., & Kivran-Swaine, F. (2010). Diamonds in the rough: Social media visual analytics for journalistic inquiry. 2010 IEEE Symposium on Visual Analytics Science and Technology. doi:10.1109/vast.2010.5652922Bustamante, A., Sebastia, L., & Onaindia, E. (2019). Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks. Sensors, 19(11), 2612. doi:10.3390/s19112612Yasseri, T., Quattrone, G., & Mashhadi, A. (2013). Temporal analysis of activity patterns of editors in collaborative mapping project of OpenStreetMap. Proceedings of the 9th International Symposium on Open Collaboration. doi:10.1145/2491055.2491068Jilani, M., Corcoran, P., & Bertolotto, M. (2013). Multi-granular Street Network Representation towards Quality Assessment of OpenStreetMap Data. Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science - IWCTS ’13. doi:10.1145/2533828.2533833Luxen, D., & Vetter, C. (2011). Real-time routing with OpenStreetMap data. Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’11. doi:10.1145/2093973.2094062Baumbach, S., Rubel, C., Ahmed, S., & Dengel, A. (2019). Geospatial Customer, Competitor and Supplier Analysis for Site Selection of Supermarkets. Proceedings of the 2019 2nd International Conference on Geoinformatics and Data Analysis. doi:10.1145/3318236.3318264Milot, J., Munroe, P., Beaudry, E., Grondin, F., & Bourdeau, G. (2016). Lookupia. Proceedings of the 25th International Conference Companion on World Wide Web - WWW ’16 Companion. doi:10.1145/2872518.2890485Ciepluch, B., Mooney, P., Jacob, R., & Winstanley, A. C. (2009). Using OpenStreetMap to deliver location-based environmental information in Ireland. SIGSPATIAL Special, 1(3), 17-22. doi:10.1145/1645424.1645428Del Pilar Salas-Zárate, M., López-López, E., Valencia-García, R., Aussenac-Gilles, N., Almela, Á., & Alor-Hernández, G. (2014). A study on LIWC categories for opinion mining in Spanish reviews. Journal of Information Science, 40(6), 749-760. doi:10.1177/0165551514547842Gambino, O. J., & Calvo, H. (2016). A Comparison Between Two Spanish Sentiment Lexicons in the Twitter Sentiment Analysis Task. Advances in Artificial Intelligence - IBERAMIA 2016, 127-138. doi:10.1007/978-3-319-47955-2_11Mooney, P., Corcoran, P., & Winstanley, A. C. (2010). Towards quality metrics for OpenStreetMap. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’10. doi:10.1145/1869790.1869875El-Ashmawy, K. l. A. (2016). TESTING THE POSITIONAL ACCURACY OF OPENSTREETMAP DATA FOR MAPPING APPLICATIONS. Geodesy and cartography, 42(1), 25-30. doi:10.3846/20296991.2015.116049

Using OpenStreetMap to deliver location-based environmental information in Ireland

This paper outlines research work-in-progress on delivery of location-based services for environmental information in Ireland. A prototype web map service (WMS) is developed to deliver map-based environmental information using a specially customised version of the OpenStreetMap database. This WMS must deliver a location-based information package to the user: maps of the area that the user is viewing, key state-of-the-environment indicator information for that geographical area, and links to where the actual data and further environmental information can be obtained. This information package must be presented in a way that best matches the environmental preferences of the user. These preferences are derived from a set of 'user profiles' of potential users of the WMS. Software tools developed during this work to derive geospatial products from the OpenStreetMap database are described and some of our observations of working with OpenStreetMap are discussed. The paper closes with the likely directions for the continuation of this research

Management of spatial data for visualization on mobile devices

Vector-based mapping is emerging as a preferred format in Location-based Services(LBS), because it can deliver an up-to-date and interactive map visualization. The Progressive Transmission(PT) technique has been developed to enable the ecient transmission of vector data over the internet by delivering various incremental levels of detail(LoD). However, it is still challenging to apply this technique in a mobile context due to many inherent limitations of mobile devices, such as small screen size, slow processors and limited memory. Taking account of these limitations, PT has been extended by developing a framework of ecient data management for the visualization of spatial data on mobile devices. A data generalization framework is proposed and implemented in a software application. This application can signicantly reduce the volume of data for transmission and enable quick access to a simplied version of data while preserving appropriate visualization quality. Using volunteered geographic information as a case-study, the framework shows exibility in delivering up-to-date spatial information from dynamic data sources. Three models of PT are designed and implemented to transmit the additional LoD renements: a full scale PT as an inverse of generalisation, a viewdependent PT, and a heuristic optimised view-dependent PT. These models are evaluated with user trials and application examples. The heuristic optimised view-dependent PT has shown a signicant enhancement over the traditional PT in terms of bandwidth-saving and smoothness of transitions. A parallel data management strategy associated with three corresponding algorithms has been developed to handle LoD spatial data on mobile clients. This strategy enables the map rendering to be performed in parallel with a process which retrieves the data for the next map location the user will require. A viewdependent approach has been integrated to monitor the volume of each LoD for visible area. The demonstration of a exible rendering style shows its potential use in visualizing dynamic geoprocessed data. Future work may extend this to integrate topological constraints and semantic constraints for enhancing the vector map visualization