Pricing Schemes for Metropolitan Traffic Data Markets

Negin Golrezaei, Hamid Nazerzadeh

Abstract

Data marketplaces provide platforms for management of large data sets. The data markets are rapidly growing, yet the pricing strategies for data and data analytics are not yet well-understood. In this paper, we explore some of the pricing schemes applicable to data marketplaces in the context of transportation traffic data. This includes historical and real-time freeway and arterial congestion data. We investigate pricing raw sensor data vs. processed information (e.g, prediction of traffic patterns or route planning services) and show that, under natural assumptions, the raw data should be priced higher than processed information.

References

  1. ADMS (2009). Adms smart travel lab. http://cts.virginia.edu/stl-adms.htm/.
  2. Gehrke, J. D. and Wojtusiak, J. (2008). Traffic prediction for agent route planning. In Computational ScienceICCS 2008, pages 692-701. Springer.
  3. Harmon, R., Demirkan, H., Hefley, B., and Auseklis, N. (2009). Pricing strategies for information technology services: A value-based approach. In System Sciences, 2009. HICSS'09. 42nd Hawaii International Conference on, pages 1-10. IEEE.
  4. Ishibashi, K. and Kaneko, T. (2008). Partial privatization in mixed duopoly with price and quality competition. Journal of Economics, 95(3):213-231.
  5. Klein, L. A. (2001). Sensor technologies and data requirements for ITS.
  6. Knaian, A. N. (2000). A wireless sensor network for smart roadbeds and intelligent transportation systems. PhD thesis, Massachusetts Institute of Technology.
  7. Levin, J. (2003). Supermodular games. Lectures Notes, Department of Economics, Stanford University.
  8. Lohr, S. (2011). New ways to exploit raw data may bring surge of innovation, a study says. The New York Times. Available at http://www.nytimes.com/2011/05/13/ technology/13data.html.
  9. Muschalle, A., Stahl, F., L öser, A., and Vossen, G. (2013). Pricing approaches for data markets. In Enabling Real-Time Business Intelligence, pages 129- 144. Springer.
  10. Pan, B., Demiryurek, U., and Shahabi, C. (2012). Utilizing real-world transportation data for accurate traffic prediction. In ICDM, pages 595-604.
  11. Park, B., Messer, C. J., and Urbanik II, T. (1998). Shortterm freeway traffic volume forecasting using radial basis function neural network. Transportation Research Record: Journal of the Transportation Research Board, 1651(1):39-47.
  12. Schomm, F., Stahl, F., and Vossen, G. (2013). Marketplaces for data: an initial survey. ACM SIGMOD Record, 42(1):15-26.
  13. Shapiro, C., Varian, H. R., and Becker, W. (1999). Information rules: a strategic guide to the network economy. Journal of Economic Education, 30:189-190.
  14. Tubaishat, M., Zhuang, P., Qi, Q., and Shang, Y. (2009). Wireless sensor networks in intelligent transportation systems. Wireless communications and mobile computing, 9(3):287-302.
  15. Williams, B. M., Durvasula, P. K., and Brown, D. E. (1998). Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transportation Research Record: Journal of the Transportation Research Board, 1644(1):132-141.
  16. Yuan, J., Zheng, Y., Xie, X., and Sun, G. (2011). Driving with knowledge from the physical world. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 316-324. ACM.
Download


Paper Citation


in Harvard Style

Golrezaei N. and Nazerzadeh H. (2014). Pricing Schemes for Metropolitan Traffic Data Markets . In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-035-2, pages 266-271. DOI: 10.5220/0005106602660271


in Bibtex Style

@conference{data14,
author={Negin Golrezaei and Hamid Nazerzadeh},
title={Pricing Schemes for Metropolitan Traffic Data Markets},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2014},
pages={266-271},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005106602660271},
isbn={978-989-758-035-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Pricing Schemes for Metropolitan Traffic Data Markets
SN - 978-989-758-035-2
AU - Golrezaei N.
AU - Nazerzadeh H.
PY - 2014
SP - 266
EP - 271
DO - 10.5220/0005106602660271