Advertisement

Enabling crowdsensing-based road condition monitoring service by intermediary

  • Kevin Laubis
  • Marcel Konstantinov
  • Viliam Simko
  • Alexander Gröschel
  • Christof Weinhardt
Research Paper

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.

Keywords

Crowdsensing Internet of things Road condition monitoring Multi-sourcing Service integration Hotspot analysis 

JEL Classification

C8 C13 C32 H54 L86 

References

  1. Allmendinger, G., & Lombreglia, R. (2005). Four strategies for the age of smart services. Harvard Business Review, 83(10), 131.Google Scholar
  2. Anselin, L. (1995). Local indicators of spatial association - LISA. Geographical Analysis, 27(2), 93–115.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.Google Scholar
  7. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRefGoogle Scholar
  8. 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).Google Scholar
  9. 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.Google Scholar
  10. 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.Google Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.Google Scholar
  15. Farhangi, H. (2010). The path of the smart grid. IEEE Power and Energy Magazine, 8(1), 18–28.CrossRefGoogle Scholar
  16. Forslöf, L., & Jones, H. (2015). Roadroid: continuous road condition monitoring with smart phones. Journal of Civil Engineering and Architecture, 9(4), 485–496.Google Scholar
  17. 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).Google Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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).Google Scholar
  20. 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).Google Scholar
  21. 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.Google Scholar
  22. 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.CrossRefGoogle Scholar
  23. Hand, J.R., & Lev, B. (2003). Intangible assets: values, measures, and risks. Oxford: OUP Oxford.Google Scholar
  24. 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.Google Scholar
  25. Hevner, A.R., March, S.T., Park, J., Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.CrossRefGoogle Scholar
  26. Kohlmann, F., Börner, R., Alt, R. (2010). A framework for the design of service maps. In Americas conference on information systems amcis2010.Google Scholar
  27. 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.Google Scholar
  28. 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.Google Scholar
  29. 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.Google Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. 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.Google Scholar
  33. Ord, J.K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27(4), 286–306.CrossRefGoogle Scholar
  34. O’Sullivan, D., & Unwin, D. (2002). Geographic information analysis. New York: Wiley.Google Scholar
  35. 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.CrossRefGoogle Scholar
  36. Puterman, M.L. (1994). Markov decision processes: discrete stochastic dynamic programming, 1st Edn. New York: Wiley.CrossRefGoogle Scholar
  37. 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).Google Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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.Google Scholar
  40. Schölkopf, B. (2006). Learning with kernels: support vector machines, regularization, optimization and beyond. Cambridge: MIT Press.Google Scholar
  41. Spohrer, J., & Maglio, P.P. (2010). Service Science: toward a smarter planet. In Introduction to service engineering (pp. 1–30). Hoboken: Wiley.Google Scholar
  42. 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.CrossRefGoogle Scholar
  43. 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.CrossRefGoogle Scholar
  44. Torrence, C., & Compo, G. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.CrossRefGoogle Scholar
  45. 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.Google Scholar
  46. 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.CrossRefGoogle Scholar
  47. 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.Google Scholar
  48. 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.Google Scholar
  49. Yagi, K. (2014). Collecting Pavement Big Data by using Smartphone (Tech. Rep.). Bali.Google Scholar
  50. 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.CrossRefGoogle Scholar

Copyright information

© Institute of Applied Informatics at University of Leipzig 2018

Authors and Affiliations

  1. 1.FZI Research Center for Information TechnologyInformation Process Engineering (IPE)KarlsruheGermany
  2. 2.Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM)KarlsruheGermany

Personalised recommendations