Abstract
Mobility in urban environments is an undisputed key factor that can affect citizens’ well-being and quality of life. This is particularly relevant for those people with disabilities or with reduced mobility who have to face the presence of barriers in urban areas. In this scenario, the availability of information about such architectural elements (together with facilities) can greatly support citizens’ mobility by enhancing their independence and their abilities in conducting daily outdoor activities. With this in mind, we have designed and developed mobile Pervasive Accessibility Social Sensing (mPASS), a system that provides users with personalized paths, computed on the basis of their own preferences and needs, with a customizable and accessible interface. The system collects data from crowdsourcing and crowdsensing to map urban and architectural accessibility by providing reliable information coming from different data sources with different levels of trustworthiness. In this context, reliability can be ensured by properly managing crowdsourced and crowdsensed data, combined when possible with authoritative datasets, provided by disability rights organizations and local authorities. To demonstrate this claim, in this article we present our trustworthiness model and discuss results we have obtained by simulations.
- Naelah Al-Dabbous, Anwar Al-Yatama, and Kassem Saleh. 2011. Assessment of the trustworthiness of e-service providers. In Proceedings of the 2nd Kuwait Conference on e-Services and e-Systems (KCESS'11). ACM, Article 24. DOI:10.1145/2107556.2107580. Google Scholar
Digital Library
- Claudio Biancalana, Fabio Gasparetti, Alessandro Micarelli, and Giuseppe Sansonetti. 2013. An approach to social recommendation for context-aware mobile services. ACM Trans. Intell. Syst. Technol. 4, 1 (2013), Article 10. DOI:10.1145/2414425.2414435 Google Scholar
Digital Library
- Mohamed Bishr and Lefteris Mantelas. 2008. A trust and reputation model for filtering and classifying knowledge about urban growth. GeoJ. 72, 3--4, 229--237. DOI:10.1007/s10708-008-9182-4Google Scholar
Cross Ref
- Armir Bujari, Bogdan Licar, and Claudio E. Palazzi. 2012. Movement pattern recognition through smartphone's accelerometer. In Proceedings of the International Conference on Consumer Communications and Networking Conference (CCNC’12). IEEE, 502--506. DOI:10.1109/CCNC.2012.6181029 Google Scholar
Cross Ref
- Carlos Cardonha, Diego Gallo, Priscilla Avegliano, Ricardo Herrmann, Fernando Koch, and Sergio Borger. 2013. A crowdsourcing platform for the construction of accessibility maps. In Proceedings of the 10th International Cross-disciplinary Conference on Web Accessibility (W4A’13). ACM, 26. DOI:10.1145/2461121.2461129 Google Scholar
Digital Library
- Gunjan Chugh, Divya Bansal, and Sanjeev Sofat. 2014. Road condition detection using smartphone sensors: A survey. Int. J. Electron. Electr. Eng. 7, 6 (2014), 595--601.Google Scholar
- Alan Cooper. 2004. The inmates are running the asylum: [Why high-tech products drive us crazy and how to restore the sanity]. Sams, Indianapolis, IN.Google Scholar
- Andrew J. Flanagin and Miriam J. Metzger. 2008. The credibility of volunteered geographic information. GeoJ. 72, 3--4 (2008), 137--148, Springer Netherlands. DOI:10.1007/s10708-008-9188-y Google Scholar
Cross Ref
- Andrew J. Flanagin and Miriam J. Metzger. 2013. Trusting expert-versus user-generated ratings online: The role of information volume, valence, and consumer characteristics. Comput. Hum. Behav. 29, 4 (2013), 1626--1634. DOI:10.1016/j.chb.2013.02.001 Google Scholar
Digital Library
- Avigdor Gal. 2014. Uncertain entity resolution: re-evaluating entity resolution in the big data era: Tutorial. In Proc. VLDB Endow. 7, 13, 1711--1712. DOI:10.14778/2733004.2733068 Google Scholar
Digital Library
- Rudolf Giffinger, Christian Fertner, Hans Kramar, Robert Kalasek, Natasa Pichler-Milanovic, and Evert Meijers. 2007. Smart cities. Ranking of European medium-sized cities. Final Report, Centre of Regional Science, Vienna UT, 303--320.Google Scholar
- Manish Gupta and Jiawei Han. 2011. Heterogeneous network-based trust analysis: A survey. ACM SIGKDD Explor. Newslett. 13, 1 (2011), 54--71. DOI:10.1145/2031331.2031341 Google Scholar
Digital Library
- Yusuke Iwasawa, Kouya Nagamine, Ikuko Eguchi Yairi, and Yutaka Matsuo. 2015. Toward an automatic road accessibility information collecting and sharing based on human behavior sensing technologies of wheelchair users. Procedia Comput. Sci. 63 (2015), 74--81. Google Scholar
Cross Ref
- Steve Jacobs. 1999. Section 255: Fueling the creation of new electronic curbcuts. Telecommun. Indust. Assoc. Access 7 (1999), 16--99.Google Scholar
- Carsten Keßler, Krzysztof Janowicz, and Mohamed Bishr. 2009. An agenda for the next generation gazetteer: Geographic information contribution and retrieval. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, New York, 91--100. Google Scholar
Digital Library
- Mikkel Baun Kjærgaard, Martin Wirz, Daniel Roggen, and Gerhard Tröster. 2012. Detecting pedestrian flocks by fusion of multi-modal sensors in mobile phones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp’12). ACM, 240--249. DOI:10.1145/2370216.2370256 Google Scholar
Digital Library
- Anders Lindgren, Fang Chen, Per Amdahl, and Per Chaikiat. 2007. Using personas and scenarios as an interface design tool for advanced driver assistance systems. In Universal Access in Human-Computer Interaction: Ambient Interaction, 460--469. Google Scholar
Cross Ref
- Sean Luke, Claudio Cioffi-Revilla, Liviu Panait, Keith Sullivan, and Gabriel Balan. 2005. MASON: A multiagent simulation environment. Simulation. 81, 7 (2005), 517--527. DOI:http://dx.doi.org/10.1177/0037549705058073 Google Scholar
Digital Library
- Afra J. Mashhadi and Licia Capra. 2011. Quality control for real-time ubiquitous crowdsourcing. In Proceedings of the 2nd International Workshop on Ubiquitous Crowdsourcing (UbiCrowd'11). ACM, New York, 5--8. DOI:http://dx.doi.org/10.1145/2030100.2030103 Google Scholar
Digital Library
- Afra J. Mashhadi, Giovanni Quattrone, Licia Capra, and Peter Mooney. 2012. On the accuracy of urban crowd-sourcing for maintaining large-scale geospatial databases. In Proceedings of the 8th Annual International Symposium on Wikis and Open Collaboration (WikiSym'12). ACM, New York, Article No. 15. DOI:http://dx.doi.org/10.1145/2462932.2462952 Google Scholar
Digital Library
- Gavin McArdle and Rob Kitchin. 2016. Improving the veracity of open and real-time urban data. Built Environ. 42, 3 (2016): 457--473. Google Scholar
Cross Ref
- Miriam J. Metzger and Andrew J. Flanagin. 2013. Credibility and trust of information in online environments: The use of cognitive heuristics. J. Pragmat. (2013), 1--11. DOI:http://dx.doi.org/10.1016/j.pragma.2013.07.12Google Scholar
- Silvia Mirri, Catia Prandi, and Paola Salomoni. 2014. A context aware system for personalized and accessible pedestrian paths. In Proceedings of the International Conference on High Performance Computing 8 Simulation (HPCS’14), 833--840. DOI:http://dx.doi.org/10.1109/HPCSim.2014.6903776 Google Scholar
Cross Ref
- Silvia Mirri, Catia Prandi, and Paola Salomoni. 2016. Personalizing pedestrian accessible way-finding with mPASS. In Proceedings of the 13th IEEE Annual Consumer Communications 8 Networking Conference (CCNC’16), 1119--1124. DOI:10.1109/CCNC.2016.7444946 Google Scholar
Digital Library
- Silvia Mirri, Catia Prandi, Paola Salomoni, Franco Callegati, and Aldo Campi. 2014. On combining crowdsourcing, sensing and open data for an accessible smart city. In Proceedings of the 8th Next Generation Mobile Apps, Services and Technologies (NGMAST’14), 294--299. DOI:10.1109/NGMAST.2014.59 Google Scholar
Digital Library
- Silvia Mirri, Catia Prandi, Paola Salomoni, Franco Callegati, Andrea Melis, and Marco Prandini. 2016. A service-oriented approach to crowdsensing for accessible smart mobility scenarios. Mobile Information Systems, 2016.Google Scholar
- Klaus Miesenberger. 2012. Accessible maps. W3C Research and Development Working Group Wiki. Retrieved April 2016 from http://www.w3.org/WAI/RD/wiki/Accessible_Maps.Google Scholar
- Kaixiang Mo, Erheng Zhong, and Qiang Yang. 2013. Cross-task crowdsourcing. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 677--685. DOI:10.1145/2487575.2487593 Google Scholar
Digital Library
- Office of National Statistics. 2002. Living in britain: Results from the 2001 general household survey. HMSO, Norwich.Google Scholar
- Jarutas Pattanaphanchai, Kieron O'Hara, and Wendy Hall. 2013. Trustworthiness criteria for supporting users to assess the credibility of web information. In Proceedings of the 22nd International Conference on World Wide Web. ACM, New York, 1123--1130. DOI:10.1145/2487788.2488132 Google Scholar
Digital Library
- Catia Prandi, Paola Salomoni, and Silvia Mirri. 2014. mPASS: Integrating People Sensing and Crowdsourcing to Map Urban Accessibility. In Proceedings of the 13th IEEE Annual Consumer Communications 8 Networking Conference (CCNC’14). 10--13. Google Scholar
Cross Ref
- Catia Prandi, Stefano Ferretti, Silvia Mirri, and Paola Salomoni. 2015. Trustworthiness in crowd- sensed and sourced georeferenced data. In Proceedings of the International Conference on Pervasive Computing and Communications (PerCom’15). IEEE, 402--407. Google Scholar
Cross Ref
- Catia Prandi, Valentina Nisi, Paola Salomoni, and Nuno Jardim Nunes. 2015. From gamification to pervasive game in mapping urban accessibility. In Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter. ACM, 126--129. DOI:10.1145/2808435.2808449 Google Scholar
Digital Library
- Catia Prandi, Marco Roccetti, Paola Salomoni, Valentina Nisi, and Nuno Jardim Nunes. 2017. Fighting exclusion: A multimedia mobile app with zombies and maps as a medium for civic engagement and design. Multimed. Tools Appl. 76, 4 (2017): 4951--4979. Google Scholar
Digital Library
- Vikas C. Raykarm, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. 2010. Learning from crowds. J. Mach. Learn. Res. 11, 1297--1322.Google Scholar
- Christoph Schlieder and Olga Yanenko. 2010. Spatio-temporal proximity and social distance: A confirmation framework for social reporting. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks. ACM, 60--67. DOI:10.1145/1867699.1867711 Google Scholar
Digital Library
- Trenton Schulz and Kristin Skeide Fuglerud. 2012. Creating personas with disabilities. In Proceedings of the International Conference on Computers for Handicapped Persons. Springer, Berlin, 145--152. Google Scholar
Digital Library
- Vidya Setlur, Cynthia Kuo, and Peter Mikelsons. 2010. Towards designing better map interfaces for the mobile: Experiences from example. In Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research and Application (COM.Geo'10). DOI:http://dx.doi.org/10.1145/1823854.1823890 Google Scholar
Digital Library
- Jutta Treviranus. 2014. Leveraging the web as a platform for economic inclusion. Behav. Sci. Law 32, 1 (2014), 94--103. DOI:10.1002/bsl.2105 Google Scholar
Cross Ref
- Matteo Venanzi, Alex Rogers, and Nicholas R. Jennings. 2013. Trust-based fusion of untrustworthy information in crowdsourcing applications. In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’13). International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, 829--836.Google Scholar
Digital Library
- Jeroen Vuurens, Arjen P. de Vries, and Carsten Eickhoff. 2011. How much spam can you take? An analysis of crowdsourcing results to increase accuracy. In Proceedings of ACM SIGIR Workshop on Crowdsourcing for Information Retrieval (CIR’11), ACM, New York, 21--26.Google Scholar
- World Health Organization, 2011. World report on disability. Retrieved April 2016 from http://www.who.int/disabilities/world_report/2011/accessible_en.pdf.Google Scholar
- World Health Organization. 2016. Urban health: major opportunities for improving global health outcomes, despite persistent health inequities. Retrieved April 2016 from http://www.who.int/mediacentre/news/releases/2016/urban-health-report/en/.2016Google Scholar
- Franco Zambonelli. 2011. Pervasive urban crowdsourcing: Visions and challenges. In Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM’11). IEEE, 578--583. DOI:10.1109/PERCOMW.2011.5766956 Google Scholar
Cross Ref
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On the Need of Trustworthy Sensing and Crowdsourcing for Urban Accessibility in Smart City
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