Journal of Computer Science and Technology

, Volume 29, Issue 4, pp 562–575 | Cite as

Emerging Applications for Cyber Transportation Systems

  • Aditya Wagh
  • Yunfei Hou
  • Chunming Qiao
  • Longfei Zhang
  • Xu Li
  • Adel Sadek
  • Kevin Hulme
  • Changxu Wu
  • Hong-Li Xu
  • Liu-Sheng Huang
Survey

Abstract

Recent advances in connected vehicles and autonomous driving are going to change the face of ground transportation as we know it. This paper describes the design and evaluation of several emerging applications for such a cyber transportation system (CTS). These applications have been designed using holistic approaches, which consider the unique roles played by the human drivers, the transportation system, and the communication network. They can improve driver safety and provide on-road infotainment. They can also improve transportation operations and efficiency, thereby benefiting travelers and attracting investment from both government agencies and private businesses to deploy infrastructures and bootstrap the evolutionary process of CTS.

Keywords

emerging technology application algorithm/protocol design and analysis cyber transportation system 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

11390_2014_1450_MOESM1_ESM.pdf (73 kb)
ESM 1(PDF 73 kb)

References

  1. [1]
    Slavik M, Mahgoub I. Spatial distribution and channel quality adaptive protocol for multihop wireless broadcast routing in VANET. IEEE Transactions on Mobile Computing, 2013, 12(4): 722-734.CrossRefGoogle Scholar
  2. [2]
    Guo L, Huang S, Sadek A W. An evaluation of likely environmental benefits of a time-dependent green routing system in the greater Buffalo-Niagara region. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 2013, 17(1): 18-30.CrossRefGoogle Scholar
  3. [3]
    Barnum D T, Karlaftis M G, Tandon S. Improving the efficiency of metropolitan area transit by joint analysis of its multiple providers. Transportation Research Part E: Logistics and Transportation Review, 2011, 47(6): 1160-1176.CrossRefGoogle Scholar
  4. [4]
    U.S. Department of Transportation. Safety pilot program overview, www.its.dot.gov/safety_pilot/spmd.htm, May 2014.
  5. [5]
    Markoff J. Google cars drive themselves in traffic. The New York Times, October 2010. http://www.nytimes.com/2010/10/10/science/10google.html?pagewanted=all, May 2014.
  6. [6]
    Zhang Y, Wu C, Wan J, Qiao C. Development and validation of warning message utility scale (WMUS). Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2013, 57(1): 1179-1183.CrossRefGoogle Scholar
  7. [7]
    Farah H, Koutsopoulos H N, Saifuzzaman H et al. Evaluation of the effect of cooperative infrastructure-to-vehicle systems on driver behavior. Transportation Research Part C: Emerging Technologies, 2012, 21(1): 42-56.CrossRefGoogle Scholar
  8. [8]
    Marshall D, Lee J D, Austria R A. Alerts for in-vehicle information systems: Annoyance, urgency, and appropriateness. Human Factors, 2007, 49(1): 145-157.CrossRefGoogle Scholar
  9. [9]
    Jamson A H, Merat N. Surrogate in-vehicle information systems and driver behavior: Effects of visual and cognitive load in rural driving. Transportation Research Part F: Traffic Psychology and Behaviour, 2005, 8(2): 79-96.CrossRefGoogle Scholar
  10. [10]
    Donmez B, Boyle L N, Lee J D. The impact of distraction mitigation strategies on driving performance. Human Factors, 2006, 48(4): 785-804.CrossRefGoogle Scholar
  11. [11]
    Verwey W B. On-line driver workload estimation: Effects of road situation and age on secondary task measures. Ergonomics, 2000, 43(2): 187-209.CrossRefGoogle Scholar
  12. [12]
    SAE International. Its in-vehicle message priority. Standard, J2395, 2002. http://subscriptions.sae.org/content/j2395_200-202, May 2014.
  13. [13]
    Sohn H, Lee J D, Bricker D L et al. A dynamic programming algorithm for scheduling in-vehicle messages. IEEE Trans. Intelligent Transportation Systems, 2008, 9(2): 226-234.CrossRefGoogle Scholar
  14. [14]
    Wu C, Liu Y. Queuing network modeling of driver workload and performance. IEEE Trans. Intelligent Transportation Systems, 2007, 8(3): 528-537.CrossRefGoogle Scholar
  15. [15]
    Li X, Yu X, Wagh A, Qiao C. Human factors-aware service scheduling in vehicular cyber-physical systems. In Proc. IEEE International Conference on Computer Communications, April 2011, pp.2174-2182.Google Scholar
  16. [16]
    Guo M, Ammar M H, Zegura E W. V3: A vehicle-to-vehicle live video streaming architecture. In Proc. the 3rd Int. Conf. Pervasive Comp. and Commun., March 2005, pp.171-180.Google Scholar
  17. [17]
    Yoon S, Ha D T, Ngo H Q, Qiao C. MoPADS: A mobility profile aided file downloading service in vehicular networks. IEEE Trans. Vehicular Technology, 2009, 58(9): 5235-5246.CrossRefGoogle Scholar
  18. [18]
    Chu Y, Huang N. Delivering of live video streaming for vehicular communication using peer-to-peer approach. In Proc. Mobile Networking for Vehicular Environments, May 2007, pp.1-6.Google Scholar
  19. [19]
    Cheng H T, Shan H, Zhuang W. Infotainment and road safety service support in vehicular networking: From a communication perspective. Mechanical Systems and Signal Processing, 2011, 25(6): 2020-2038.CrossRefGoogle Scholar
  20. [20]
    Bucciol P, Masala E, Kawaguchi N, Takeda K, De Martin J. Performance evaluation of H. 264 video streaming over inter-vehicular 802.11 ad hoc networks. In Proc. the 16th Int. Symp. Personal, Indoor and Mobile Radio Communications, Sept. 2005, pp.1936-1940.Google Scholar
  21. [21]
    Xue J, Chen C W. A new perceptual quality metric for video transrating for mobile devices. In Proc. the 2010 ACM Multimedia Workshop on Mobile Cloud Media Computing, Oct. 2010, pp.35-40.Google Scholar
  22. [22]
    Song W, Tjondronegoro D W, Wang S et al. Impact of zooming and enhancing region of interests for optimizing user experience on mobile sports video. In Proc. the 18th ACM Int. Conf. Multimedia, Oct. 2010, pp.321-330.Google Scholar
  23. [23]
    Dobrian F, Sekar V, Awan A et al. Understanding the impact of video quality on user engagement. In Proc. the ACM SIGCOMM Conference, Aug. 2011, pp.362-373.Google Scholar
  24. [24]
    Tan W L, Lau W C, Yue O, Hui T H. Analytical models and performance evaluation of drive-thru internet systems. IEEE J. Selected Areas in Communications, 2011, 29(1): 207-222.CrossRefGoogle Scholar
  25. [25]
    He K, Li X, Schick B, Qiao C, Sudhaakar R, Addepalli S, Chen X. On-road video delivery with integrated heterogeneous wireless networks. Ad Hoc Networks, 2013, 11(7): 1992-2001.CrossRefGoogle Scholar
  26. [26]
    Morwitza V G, Steckela J H, Guptab A. When do purchase intentions predict sales? Int. J. Forecasting, 2007, 23(3): 347-364.CrossRefGoogle Scholar
  27. [27]
    Sun B, Morwitz V G. Stated intentions and purchase behavior: A unified model. International Journal of Research in Marketing, 2010, 27(4): 356-366.CrossRefGoogle Scholar
  28. [28]
    Goldfarb A, Tucker C. Online advertising. Advances in Computers, 2011, 81: 289-315.CrossRefGoogle Scholar
  29. [29]
    Liu N, Liu M, Cao J et al. When transportation meets communication: V2P over VANETs. In Proc. the 30th IEEE Int. Conf. Distributed Computing Systems, Jun. 2010, pp.567-576.Google Scholar
  30. [30]
    Deshpande P, Kashyap A, Sung C, Das S R. Predictive methods for improved vehicular WiFi access. In Proc. the 7th International Conference on Mobile Systems, Applications, and Services, June 2009, pp.263-276.Google Scholar
  31. [31]
    Ge Y, Liu C, Xiong H, Chen J. A taxi business intelligence system. In Proc. the 17th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Aug. 2011, pp.735-738.Google Scholar
  32. [32]
    Yuan J, Zheng Y, Zhang C et al. T-drive: Driving directions based on taxi trajectories. In Proc. the 18th SIGSPATIAL Int. Conf. Advances in Geographic Information Systems, Nov. 2010, pp.99-108.Google Scholar
  33. [33]
    Seow K T, Dang N H, Lee D. A collaborative multiagent taxi-dispatch system. IEEE Trans. Automation Science and Engineering, 2010, 7(3): 607-616.CrossRefGoogle Scholar
  34. [34]
    Alshamsi A, Abdallah S, Rahwan I. Multiagent self-organization for a taxi dispatch system. In Proc. the 8th Int. Conf. Autonomous Agents and Multiagent Systems, May 2009, pp.21-28.Google Scholar
  35. [35]
    Hou Y, Li X, Zhao Y et al. Towards efficient vacant taxis cruising guidance. In Proc. IEEE Global Communications Conference, Dec. 2013.Google Scholar
  36. [36]
    Zhang D, Li Y, Zhang F, Lu M, Liu Y, He T. coRide: Car-pool service with a win-win fare model for large-scale taxicab networks. In Proc. the 11th ACM Conference on Embedded Networked Sensor Systems, Nov. 2013, Article No.9.Google Scholar
  37. [37]
    Chen P, Liu J, Chen W. A fuel-saving and pollution-reducing dynamic taxi-sharing protocol in VANETs. In Proc. the 72nd IEEE Vehicular Technology Conf. Fall, Sept. 2010, pp.1-5.Google Scholar
  38. [38]
    Chen C, Shallcross D, Shih Y et al. Smart ride share with flexible route matching. In Proc. the 13th Int. Conf. Advanced Communication Technology, Feb. 2011, pp.1506-1510.Google Scholar
  39. [39]
    Hou Y, Li X, Qiao C. TicTac: From transfer-incapable car-pooling to transfer-allowed carpooling. In Proc. IEEE Global Communications Conference, Dec. 2012, pp. 268-273.Google Scholar
  40. [40]
    Zhao Y, Wagh A, Hulme K et al. Integrated traffic-driving-networking simulator: A unique R&D tool for connected vehicles. In Proc. Int. Conf. Connected Vehicles and Expo, Dec. 2012, pp.203-204.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Aditya Wagh
    • 1
  • Yunfei Hou
    • 1
  • Chunming Qiao
    • 1
  • Longfei Zhang
    • 1
  • Xu Li
    • 1
  • Adel Sadek
    • 2
  • Kevin Hulme
    • 3
  • Changxu Wu
    • 4
  • Hong-Li Xu
    • 5
  • Liu-Sheng Huang
    • 5
  1. 1.Department of Computer Science and EngineeringState University of New York at BuffaloBuffaloU.S.A.
  2. 2.Department of Civil, Structural and Environmental EngineeringState University of New York at BuffaloBuffaloU.S.A.
  3. 3.The New York State Center for Engineering Design and Industrial InnovationBuffaloU.S.A.
  4. 4.Department of Industrial and System EngineeringState University of New York at BuffaloBuffaloU.S.A.
  5. 5.School of Computer Science and Technology, Suzhou Institute for Advanced StudyUniversity of Science and Technology of ChinaSuzhouChina

Personalised recommendations