Dependable and Historic Computing pp 93-117

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6875)

Using Real-Time Road Traffic Data to Evaluate Congestion

  • Jean Bacon
  • Andrei Iu. Bejan
  • Alastair R. Beresford
  • David Evans
  • Richard J. Gibbens
  • Ken Moody

Abstract

Providing citizens with accurate information on traffic conditions can encourage journeys at times of low congestion, and uptake of public transport. The TIME project (Transport Information Monitoring Environment) has focussed on urban traffic, using the city of Cambridge as an example. We have investigated sensor and network technology for gathering traffic data, and have designed and built reusable software components to distribute, process and store sensor data in real time. Instrumenting a city to provide this information is expensive and potentially invades privacy. Increasingly, public transport vehicles are equipped with sensors to provide arrival time estimates at bus stop displays in real-time. We have shown that these data can be used for a number of purposes. Firstly, archived data can be analysed statistically to understand the behaviour of traffic under a range of “normal” conditions at different times, for example in and out of school term. Secondly, periods of extreme congestion resulting from known incidents can be analysed to show the behaviour of traffic over time. Thirdly, with such analyses providing background information, real-time data can be interpreted in context to provide more reliable and accurate information to citizens. In this paper we present some of the findings of the TIME project.

Keywords

static sensor mobile sensor traffic monitoring middleware bus probe data journey times large scale data analysis quantile regression spline interpolation 

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References

  1. 1.
    Devereux, R., Dawson, J., Dix, M., Hazel, G., Holmes, D., Glaister, S., Joseph, S., Macgowan, C.T., Nimick, B., Roberts, M., Searles, L., Turner, R., Gooding, S., Hickey, S., Rickett, W.: Feasibility study of road pricing in the UK. Technical report, Department for Transport (July 2004)Google Scholar
  2. 2.
    Boucher, A., Bowman, J., Noble, B., Hird, D., Lloyd, D., Rose, W., Shah, H., Dennis, S., Lees, A., Grieve, I., Riley, S., Robinson, D., Salathiel, D., Crane, E., Laffan, W., Medhurst, C., Beg, O., Coleman, B.: Transport statistics bulletin road statistics 2008: Traffic speeds and congestion. Technical report, Department for Transport (2009)Google Scholar
  3. 3.
    Kenyon, S., Lyons, G.: The value of integrated multimodal traveller information and its potential contribution to modal change. Transportation Research Part F: Traffic Psychology and Behaviour 6(1), 1–21 (2003)CrossRefGoogle Scholar
  4. 4.
    Ingram, D.: Reconfigurable middleware for high availability sensor systems. In: Proceedings of the Third International Conference on Distributed Event-Based Systems, DEBS 2009 (2009)Google Scholar
  5. 5.
    Hunt, P.B., Robertson, D.I., Bretherton, R.D., Winton, R.I.: SCOOT—a traffic responsive method of coordinating signals. Technical Report LR1014, Transport and Road Research Laboratory (1981)Google Scholar
  6. 6.
    Ehmke, J.F., Meisel, S., Mattfeld, D.C.: Floating car data based analysis of urban travel times for the provision of traffic quality. In: Traffic Data Collection and its Standardization. International Series in operations Research & Management Science, vol. 144, pp. 129–149. Springer Science+Business Media, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Rose, G.: Mobile phones as traffic probes: Practices, prospects and issues. Transport Reviews 26(3), 275–291 (2006)CrossRefGoogle Scholar
  8. 8.
    Naranjo, J.E., Jiménez, F., Serradilla, F.J., Zato, J.G.: Comparison between floating car data and infrastructure sensors for traffic speed estimation. In: IEEE ITSC 2010 Workshop on Emergent Cooperative Technologies in Intelligent Transportation Systems. IEEE Intelligent Transportation Systems Society, Madeira Island (2010)Google Scholar
  9. 9.
    Google: Arterial traffic available on Google (August 2009), http://google-latlong.blogspot.com/2009/08/arterial-traffic-available-on-google.html
  10. 10.
    Herrera, J.C., Work, D.B., Herring, R., Ban, X.J., Jacobson, Q., Bayen, A.M.: Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment. Transportation Research Part C (18), 568–583 (2010)Google Scholar
  11. 11.
    Herring, R., Hofleitner, A., Abbeel, P., Bayen, A.: Estimating arterial traffic conditions using sparse probe data. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 929–936. IEEE Intelligent Transportation Systems Society, Madeira Island (2010)CrossRefGoogle Scholar
  12. 12.
    Braxmeier, H., Schmidt, V., Spodarev, E.: Kriged road-traffic maps. In: Interfacing Geostatistics and GIS, pp. 105–119. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Knoop, L., Eames, T.: Public transport technology in the United Kingdom: Annual survey 2008. Technical report, RTIG Ltd. (February 2009)Google Scholar
  14. 14.
    Bertini, R.L., Tantiyanugulchai, S.: Transit buses as traffic probes: Use of geolocation data for empirical evaluation. Transportation Research Record (1870), 35–45 (2004)Google Scholar
  15. 15.
    Chakroborty, P., Kikuchi, S.: Using bus travel time data to estimate travel times on urban corridors. Transportation Research Record (1870), 18–25 (2004)Google Scholar
  16. 16.
    Pu, W., Lin, J.: Urban travel time estimation using real time bus tracking data. In: Transport Chicago 2008, Chicago, Illinois, USA (2008)Google Scholar
  17. 17.
    Pu, W., Lin, J., Lon, L.: Real-time estimation of urban street segment travel time using buses as speed probe. Transportation Research Record (2129), 81–89 (2009)Google Scholar
  18. 18.
    Bejan, A., Gibbens, R., Evans, D., Beresford, A., Bacon, J., Friday, A.: Statistical modelling and analysis of sparse bus probe data in urban areas. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 1256–1263. IEEE Intelligent Transportation Systems Society, Madeira Island (2010)CrossRefGoogle Scholar
  19. 19.
    Bejan, A., Gibbens, R.: Evaluation of velocity fields via sparse bus probe data in urban areas. In: 14th International IEEE Conference on Intelligent Transportation Systems. IEEE Intelligent Transportation Systems Society, Washington (2011)Google Scholar
  20. 20.
    Antonios Skordylis, N.T.: Delay-bounded routing in vehicular ad-hoc networks. In: ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) (2008)Google Scholar
  21. 21.
    Skordylis, A., Trigoni, N.: Jointly optimizing data acquisition and delivery in traffic monitoring VANETs. In: 24th Annual ACM Symposium on Applied Computing (ACM SAC 2009) (March 2009)Google Scholar
  22. 22.
    Skordylis, A., Trigoni, N.: Efficient data propagation in traffic monitoring vehicular networks. IEEE Transactions on Intelligent Transportation Systems (to appear, 2011)Google Scholar
  23. 23.
  24. 24.
    Haklay, M., Weber, P.: OpenStreetMap: user-generated street maps. IEEE Pervasive Computing 7(4), 12–18 (2008)CrossRefGoogle Scholar
  25. 25.
    Haklay, M.: How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and Planning B: Planning and Design 37(4), 682–703 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jean Bacon
    • 1
  • Andrei Iu. Bejan
    • 1
  • Alastair R. Beresford
    • 1
  • David Evans
    • 1
  • Richard J. Gibbens
    • 1
  • Ken Moody
    • 1
  1. 1.Computer LaboratoryUniversity of CambridgeCambridgeUK

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