Abstract
Constant monitoring of road conditions would be beneficial for road authorities as well as road users. However, this is currently not possible due to limited resources. This is because road condition monitoring is carried out by engineering companies using limited resources such as specialized vehicles and trained personnel. The ubiquity of smart devices carried by drivers, such as smartphones and the ever-increasing number of sensors installed in modern vehicles, makes it possible to provide information about the condition of the road on which the vehicle is driving. We develop a smart, crowd-based road condition monitoring service that establishes an intermediary between the crowd as data provider and the road authorities and road users as service customers. In addition to providing customers with accurate and frequent road condition information, subscribers can monetize their collected data. We prove the feasibility and usability of this smart service through analytical and descriptive evaluations.
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Allmendinger, G., & Lombreglia, R. (2005). Four strategies for the age of smart services. Harvard Business Review, 83(10), 131.
Anselin, L. (1995). Local indicators of spatial association - LISA. Geographical Analysis, 27(2), 93–115.
Anttiroiko, A.-V., Valkama, P., Bailey, S.J. (2014). Smart cities in the new service economy: building platforms for smart services. AI & Society, 29(3), 323–334.
Bapna, R., Barua, A., Mani, D., Mehra, A. (2010). Research commentary—cooperation, coordination, and governance in multisourcing: an agenda for analytical and empirical research. Information Systems Research, 21 (4), 785–795.
Barile, S., & Polese, F. (2010). Smart service systems and viable service systems: Applying systems theory to service science. Service Science, 2(1-2), 21–40.
Bhoraskar, R., Vankadhara, N., Raman, B., Kulkarni, P. (2012). Wolverine: traffic and road condition estimation using smartphone sensors. In International conference on communication systems and networks comsnets2012 (pp. 1–6). Bangalore: IEEE.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Byun, J., & Park, S. (2011). Development of a self-adapting intelligent system for building energy saving and context-aware smart services. IEEE Transactions on Consumer Electronics, 57(1).
Cha, S.-H. (2007). Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Sciences, 1(4), 300–307.
Chen, K., Lu, M., Tan, G., Wu, J. (2013). CRSM: crowdsourcing based road surface monitoring. In IEEE international conference on high performance computing and communications hpcc2013 & ieee international conference on embedded and ubiquitous computing euc2013 (pp. 21512158). IEEE.
Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud. Decision Support Systems, 55(1), 412–421.
Dennis, E., Hong, Q., Wallace, R., Tansil, W., Smith, M. (2014). Pavement condition monitoring with crowdsourced connected vehicle data. Transportation Research Record: Journal of the Transportation Research Board, 2460, 31–38.
Dibbern, J., Goles, T., Hirschheim, R., Jayatilaka, B. (2004). Information systems outsourcing: a survey and analysis of the literature. ACM Sigmis Database, 35(4), 6–102.
Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., Balakrishnan, H. (2008). The pothole patrol: using a mobile sensor network for road surface monitoring. In International conference on mobile systems, applications, and services mobisys2008 (pp. 29–39). New York: ACM.
Farhangi, H. (2010). The path of the smart grid. IEEE Power and Energy Magazine, 8(1), 18–28.
Forslöf, L., & Jones, H. (2015). Roadroid: continuous road condition monitoring with smart phones. Journal of Civil Engineering and Architecture, 9(4), 485–496.
Gallivan, M.J., & Oh, W. (1999). Analyzing it outsourcing relationships as alliances among multiple clients and vendors. In Hawaii international conference on systems sciences hicss1999 (pp. 15–pp).
Gao, H., & Zhang, X. (2013). A markov-based road maintenance optimization model considering user costs. Computer-Aided Civil and Infrastructure Engineering, 28(6), 451–464.
Goldberg, M., Kieninger, A., Fromm, H. (2014). Organizational models for the multi-sourcing service integration and management function. In IEEE conference on business informatics cbi2014 (Vol. 2, pp. 101–107).
Goldberg, M., Kieninger, A., Satzger, G., Fromm, H. (2014). Transition and delivery challenges of retained organizations in it outsourcing. In International conference on exploring services science (pp. 56–71).
Goldberg, M., Satzger, G., Kieninger, A. (2015). A capability framework for it service integration and management in multi-sourcing. In European conference on information systems ecis2015.
Goovaerts, P., & Jacquez, G.M. (2005). Detection of temporal changes in the spatial distribution of cancer rates using local morans i and geostatistically simulated spatial neutral models. Journal of Geographical Systems, 7(1), 137–159.
Hand, J.R., & Lev, B. (2003). Intangible assets: values, measures, and risks. Oxford: OUP Oxford.
Herz, T.P., Hamel, F., Uebernickel, F., Brenner, W. (2010). Deriving a research agenda for the management of multisourcing relationships based on a literature review. In Americas conference on information systems amcis2010.
Hevner, A.R., March, S.T., Park, J., Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.
Kohlmann, F., Börner, R., Alt, R. (2010). A framework for the design of service maps. In Americas conference on information systems amcis2010.
Laubis, K., Simko, V., Schuller, A. (2016a). Crowd Sensing of Road Conditions and its Monetary Implications on Vehicle Navigation. In International conference on internet of people iop2016 (pp. 833–840). Toulouse: IEEE.
Laubis, K., Simko, V., Schuller, A. (2016b). Road condition measurement and assessment: A crowd based sensing approach. In International conference on information systems icis2016. Dublin: AIS.
Laubis, K., Simko, V., Schuller, A., Weinhardt, C. (2017). Road condition estimation based on heterogeneous extended oating car data. In Hawaii international conference on system sciences hicss2017 (pp. 1582–1591). Waikoloa: AIS.
Maglio, P.P., Vargo, S.L., Caswell, N., Spohrer, J. (2009). The service system is the basic abstraction of service science. Information Systems and e-Business Management, 70(4), 395–406.
Masino, J., Pinay, J., Reischl, M., Gauterin, F. (2017). Road surface prediction from acoustical measurements in the tire cavity using support vector machine. Applied Acoustics, 125, 41–48.
Mohan, P., Padmanabhan, V.N., Ramjee, R. (2008). Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In ACM conference on embedded network sensor systems sensys2008 (pp. 323–336). New York: ACM.
Ord, J.K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27(4), 286–306.
O’Sullivan, D., & Unwin, D. (2002). Geographic information analysis. New York: Wiley.
Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77.
Puterman, M.L. (1994). Markov decision processes: discrete stochastic dynamic programming, 1st Edn. New York: Wiley.
Rajamäki, J., & Vuorinen, M. (2013). Multi-supplier integration management for public protection and disaster relief (ppdr) organizations. In International conference on information networking icoin2013 (pp. 499–504).
Ratcliffe, J.H., Taniguchi, T., Groff, E.R., Wood, J.D. (2011). The philadelphia food patrol experiment: a randomized controlled trial of police patrol effectiveness in violent crime hotspots. Criminology, 49(3), 795–831.
Sayers, M.W., Gillespie, T.D., Queiroz, C.A.V. (1986). The international road roughness experiment: establishing correlation and a calibration standard for measurements. (Tech. Rep. No 45). Washington: The World Bank.
Schölkopf, B. (2006). Learning with kernels: support vector machines, regularization, optimization and beyond. Cambridge: MIT Press.
Spohrer, J., & Maglio, P.P. (2010). Service Science: toward a smarter planet. In Introduction to service engineering (pp. 1–30). Hoboken: Wiley.
Steenberghen, T., Dufays, T., Thomas, I., Flahaut, B. (2004). Intra-urban location and clustering of road accidents using gis: a belgian example. International Journal of Geographical Information Science, 18(2), 169–181.
Sugumaran, R., Larson, S.R., DeGroote, J.P. (2009). Spatio-temporal cluster analysis of county-based human west nile virus incidence in the continental united states. International Journal of Health Geographics, 8(1), 43.
Torrence, C., & Compo, G. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.
Unterharnscheidt, P., & Kieninger, A. (2010). Service level management challenges and their relevance from the customers’ point of view. In Americas conference on information systems amcis2010.
Venable, J., Pries-Heje, J., Baskerville, R. (2016). FEDS: a framework for evaluation in design science research. European Journal of Information Systems, 25(1), 77–89.
Wang, R.-Y., Chuang, Y.-T., Yi, C.-W. (2016). A crowdsourcing-based road anomaly classification system. In Asia-pacific network operations and management symposium apnoms2016: IEEE.
Watanatada, T., Harral, C., Paterson, W., Dhareshwar, A., Bhandari, A., Tsunokawa, K. (1987). The highway design and maintenance model: description of the HDM-III model, the highway design and maintenance standards series, Transportation department, Washington DC, 1 and 2, 1–47.
Yagi, K. (2014). Collecting Pavement Big Data by using Smartphone (Tech. Rep.). Bali.
Zhang, X., Yang, Z., Sun, W., Liu, Y., Tang, S., Xing, K., Mao, X. (2016). Incentives for mobile crowd sensing: a survey. IEEE Communications Surveys & Tutorials, 18(1), 54–67.
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Responsible Editors: Jan Fabian Ehmke and Rainer Alt
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Laubis, K., Konstantinov, M., Simko, V. et al. Enabling crowdsensing-based road condition monitoring service by intermediary. Electron Markets 29, 125–140 (2019). https://doi.org/10.1007/s12525-018-0292-7
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DOI: https://doi.org/10.1007/s12525-018-0292-7
Keywords
- Crowdsensing
- Internet of things
- Road condition monitoring
- Multi-sourcing
- Service integration
- Hotspot analysis