||Providing Incentives for a Road Traffic Prediction System based on Participatory Sensing
||Institute of Computer Science and Information Engineering
Traffic Prediction System
The prediction accuracy of road traffic prediction systems are based on sufficient and validate input data. Comparing with fixed installation of road side sensors, Using GPS probe vehicles incorporating with participatory sensing to collect traffic data is a more scalable and efficient method. On the other hand, when the device owner are participant, how to providing an incentive mechanism to promote users’ contribution becomes an important issue. In this paper, we propose a new incentive mechanism for participatory sensing based road traffic prediction system. Users could earn virtual credits by uploading their data, and they need to pay credits when they want to know the future traffic condition by accessing our prediction service. To define a reasonable price of users’ data, we quantify the contribution of it, and the quantified contribution is used as the price to encourage people to collect more useful data. We use a detailed vehicular simulator to evaluate our incentive mechanism. In the first experiment, we prove that the proposed pricing scheme could distinguish the quality of data. Data which can’t reflect the real road speed will have lower price. In the second experiment, we analyze the relationship between the number of nodes, variation of speed, and prediction accuracy. Finally, we let the nodes follow the current price of data on roads to decide their route. The experiment result shows that the proposed incentive mechanism could improve the prediction accuracy in evidence when the node number is large.
誌 謝 III
1. Introduction 1
2. Related Works 4
3. System Overview 9
3.1 Architecture 9
3.2 Prediction model 10
3.3 Incentive mechanisms 11
4. Simulation 13
5. Conclusion 17
 A. R. Beresford and J. Bacon, "Intelligent Transportation Systems," Pervasive Computing, IEEE, vol. 5, pp. 63-67, 2006.
 A. I. Bejan, R. J. Gibbens, D. Evans, A. R. Beresford, J. Bacon, and A. Friday, "Statistical modelling and analysis of sparse bus probe data in urban areas," in Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, 2010, pp. 1256-1263.
 X. J. Ban, Y. Li, A. Skabardonis, and J. D. Margulici, "Performance Evaluation of Travel-Time Estimation Methods for Real-Time Traffic Applications," Journal of Intelligent Transportation Systems, vol. 14, pp. 54-67, 2010/05/12 2010.
 R. Bertini and S. Tantiyanugulchai, "Transit Buses as Traffic Probes: Use of Geolocation Data for Empirical Evaluation," Transportation Research Record: Journal of the Transportation Research Board, vol. 1870, pp. 35-45, 2004.
 P. Mirchandani and L. Head, "A real-time traffic signal control system: architecture, algorithms, and analysis," Transportation Research Part C: Emerging Technologies, vol. 9, pp. 415-432, 2001.
 L. D. Baskar, B. De Schutter, and H. Hellendoorn, "Hierarchical Traffic Control and Management with Intelligent Vehicles," in Intelligent Vehicles Symposium, 2007 IEEE, 2007, pp. 834-839.
 T. Nadeem, S. Dashtinezhad, C. Liao, and L. Iftode, "TrafficView: traffic data dissemination using car-to-car communication," SIGMOBILE Mob. Comput. Commun. Rev., vol. 8, pp. 6-19, 2004.
 E. Horvitz, J. Apacible, R. Sarin, and L. Liao, "Prediction, expectation, and surprise: Methods, designs, and study of a deployed traffic forecasting service," in In Twenty-First Conference on Uncertainty in Artificial Intelligence, 2005.
 J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava, "Participatory sensing," in In: Workshop on World-Sensor-Web (WSW¡¦06): Mobile Device Centric Sensor Networks and Applications, 2006, pp. 117-134.
 P. Mohan, V. N. Padmanabhan, and R. Ramjee, "Nericell: rich monitoring of road and traffic conditions using mobile smartphones," in Proceedings of the 6th ACM conference on Embedded network sensor systems, New York, NY, USA, 2008, pp. 323-336.
 P. Zhou, Y. Zheng, and M. Li, "How long to wait?: predicting bus arrival time with mobile phone based participatory sensing," presented at the Proceedings of the 10th international conference on Mobile systems, applications, and services, Low Wood Bay, Lake District, UK, 2012.
 R. K. Ganti, N. Pham, H. Ahmadi, S. Nangia, and T. F. Abdelzaher, "GreenGPS: a participatory sensing fuel-efficient maps application," in Proceedings of the 8th international conference on Mobile systems, applications, and services, New York, NY, USA, 2010, pp. 151-164.
 E. Koukoumidis, L.-S. Peh, and M. R. Martonosi, "SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory," presented at the Proceedings of the 9th international conference on Mobile systems, applications, and services, Bethesda, Maryland, USA, 2011.
 C. P. I. Van Hinsbergen, J. W. C. Van Lint, and F. M. Sanders, "Short Term Traffic Prediction Models," in Proceedings of the 14th ITS World Congress, Beijing, China, 2007.
 S. Yamaguchi and Y. Kato, "A prediction method of non-stationary road traffic noise based on fluctuation patterns of an average number of flowing vehicles," Applied Acoustics, vol. 27, pp. 103-118, 1989.
 M. Hamed, H. Al-Masaeid, and Z. Said, "Short-Term Prediction of Traffic Volume in Urban Arterials," Journal of Transportation Engineering, vol. 121, pp. 249-254, 1995.
 D. Billings and Y. Jiann-Shiou, "Application of the ARIMA Models to Urban Roadway Travel Time Prediction - A Case Study," in Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on, 2006, pp. 2529-2534.
 B. Williams and L. Hoel, "Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results," Journal of Transportation Engineering, vol. 129, pp. 664-672, 2003.
 B. L. Smith, B. M. Williams, and R. Keith Oswald, "Comparison of parametric and nonparametric models for traffic flow forecasting," Transportation Research Part C: Emerging Technologies, vol. 10, pp. 303-321, 2002.
 J. Rice and E. van Zwet, "A simple and effective method for predicting travel times on freeways," Intelligent Transportation Systems, IEEE Transactions on, vol. 5, pp. 200-207, 2004.
 D. Nikovski, N. Nishiuma, Y. Goto, and H. Kumazawa, "Univariate short-term prediction of road travel times," in Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE, 2005, pp. 1074-1079.
 S. R. Chandra and H. Al-Deek, "Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models," Journal of Intelligent Transportation Systems, vol. 13, pp. 53-72, 2009/05/20 2009.
 W. Min and L. Wynter, "Real-time road traffic prediction with spatio-temporal correlations," Transportation Research Part C: Emerging Technologies, vol. 19, pp. 606-616, 2011.
 B. Cohen, "Incentives Build Robustness in BitTorrent," ed, 2003.
 L. Deng and L. P. Cox, "LiveCompare: grocery bargain hunting through participatory sensing," in Proceedings of the 10th workshop on Mobile Computing Systems and Applications, New York, NY, USA, 2009, pp. 4:1-4:6.
 A. R. Bharambe, C. Herley, and V. N. Padmanabhan, "Analyzing and Improving a BitTorrent Networks Performance Mechanisms," in INFOCOM 2006. 25th IEEE International Conference on Computer Communications. Proceedings, 2006, pp. 1-12.
 E. Kolja, "Bandwidth Trading in Unstructured P2P Content Distribution Networks," 2006, pp. 39-48.
 Y. Cui, T. Ma, and X. Cheng, "Multi-hop access pricing in public area WLANs," in INFOCOM, 2011 Proceedings IEEE, 2011, pp. 2678 -2686.
 L. Buttyan and J.-P. Hubaux, "Enforcing service availability in mobile ad-hoc WANs," in Proceedings of the 1st ACM international symposium on Mobile ad hoc networking computing, Piscataway, NJ, USA, 2000, pp. 87-96.
 J. Crowcroft, R. Gibbens, F. Kelly, and Sven, OString, "Modelling incentives for collaboration in mobile ad hoc networks," Perform. Eval., vol. 57, pp. 427-439, August 2004.
 H. Lütkepohl, New Introduction to Multiple Time Series Analysis: Springer, 2007.
 "MOVE (MObility model generator for VEhicular networks)," ed, 2011.