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Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories

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Abstract

Accurate travel time prediction is undoubtedly of importance to both traffic managers and travelers. In highly-urbanized areas, trip-oriented travel time prediction (TOTTP) is valuable to travelers rather than traffic managers as the former usually expect to know the travel time of a trip which may cross over multiple road sections. There are two obstacles to the development of TOTTP, including traffic complexity and traffic data coverage.With large scale historical vehicle trajectory data and meteorology data, this research develops a BPNN-based approach through integrating multiple factors affecting trip travel time into a BPNN model to predict trip-oriented travel time for OD pairs in urban network. Results of experiments demonstrate that it helps discover the dominate trends of travel time changes daily and weekly, and the impact of weather conditions is non-trivial.

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References

  • Abu-Lebdeh G, Singh A K (2011). Modeling arterial travel time with limited traffic variables using conditional independence graphs & state-space neural networks. Procedia Soc Behav Sci, 16: 207–217

    Article  Google Scholar 

  • Alecsandru C, Ishak S (2004). Hybrid model-based and memory-based traffic prediction system. Transp Res Rec, 1879(1): 59–70

    Article  Google Scholar 

  • Bar-Gera H (2007). Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: a case study from Israel. Transp Res, Part C Emerg Technol, 15(6): 380–391

    Article  Google Scholar 

  • Bhaskar A, Chung E, Dumont A (2011). Arterial travel time estimation: revisiting the classical procedure. Australasian Transport Research Forum Adelaide South Australia, 34

    Google Scholar 

  • Chen D, Zhang K, Liao T (2010). Practical travel time prediction algorithms based on neural network and data fusion for urban expressway. Proceedings 2010 Sixth International Conference on Natural Computation, 4, 1754–1758. IEEE

    Google Scholar 

  • Chen M, Chien S (2001). Dynamic freeway travel-time prediction with probe vehicle data: link based versus path based. Transp Res Rec, 1768: 157–161

    Article  Google Scholar 

  • Chien S I, Kuchipudi C M (2003). Dynamic travel time prediction with real-time and historic data. J Transp Eng, 129(6): 608–616

    Article  Google Scholar 

  • Dia H (2001). An object-oriented neural network approach to short-term traffic forecasting. Eur J Oper Res, 131(2): 253–261

    Article  Google Scholar 

  • Dong C, Shao C, Richards S H, Han L D (2014). Flow rate and time mean speed predictions for the urban freeway network using state space models. Transp Res, Part C Emerg Technol, 43: 20–32

    Article  Google Scholar 

  • Dougherty M (1995). A review of neural networks applied to transport. Transp Res, Part C Emerg Technol, 3(4): 247–260

    Article  Google Scholar 

  • Dougherty M S, Cobbett M R (1997). Short-term inter-urban traffic forecasts using neural networks. Int J Forecast, 13(1): 21–31

    Article  Google Scholar 

  • Elabd S, Schlenkhoff A (2009). ANFIS and BP neural network for travel time prediction. World Acad Sci Eng Technol, 57: 116–121

    Google Scholar 

  • Fei X, Lu C C, Liu K (2011). A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transp Res, Part C Emerg Technol, 19(6): 1306–1318

    Article  Google Scholar 

  • Goh A T C (1995). Back-propagation neural networks for modeling complex systems. Artif Intell Eng, 9(3): 143–151

    Article  Google Scholar 

  • Guo J, Huang W, Williams B M (2014). Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp Res, Part C Emerg Technol, 43: 50–64

    Article  Google Scholar 

  • Herrera J C, Work D B, Herring R, Ban X, Jacobson Q, Bayen A M (2010). Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment. Transp Res, Part C Emerg Technol, 18(4): 568–583

    Article  Google Scholar 

  • Hofleitner A, Herring R, Bayen A (2012). Arterial travel time forecast with streaming data: a hybrid approach of flow modeling and machine learning. Transp Res, Part B: Methodol, 46(9): 1097–1122

    Article  Google Scholar 

  • Huisken G, Coffa A (2000). Short-term congestion prediction: comparing time series with neural networks. In: Proceedings of Road Transport Information and Control, 2000. Tenth International Conference on (Conf. Publ. No. 472), 66–69. IET

    Google Scholar 

  • Ishak S, Alecsandru C (2004). Optimizing traffic prediction performance of neural networks under various topological, input, and traffic condition settings. J Transp Eng, 130(4): 452–465

    Article  Google Scholar 

  • Jenelius E, Koutsopoulos H N (2013). Travel time estimation for urban road networks using low frequency probe vehicle data. Transp Res, Part B: Methodol, 53(4): 64–81

    Article  Google Scholar 

  • Jiang Y J, Li X (2013). Travel time prediction based on historical trajectory data. Ann GIS, 19(1): 27–35

    Article  Google Scholar 

  • Jones M, Geng Y, Nikovski D, Hirata T (2013). Predicting link travel times from floating car data. 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, 1756–1763

    Chapter  Google Scholar 

  • Karl C A, Trayford R S (2000). Delivery of real-time and predictive travel time information: experiences from a Melbourne trial. In: Proceedings of 7th World Congress on Intelligent Systems, 3513

    Google Scholar 

  • Kůrková V (1992). Kolmogorov’s theorem and multilayer neural networks. Neural Netw, 5(3): 501–506

    Article  Google Scholar 

  • Lee W H, Tseng S S, Tsai S H (2009). A knowledge based real-time travel time prediction system for urban network. Expert Syst Appl, 36 (3): 4239–4247

    Article  Google Scholar 

  • Li B, Zhang D, Sun L, Chen C, Li S, Qi G, Yang Q (2011). Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Seattle, WA, 63–68

    Chapter  Google Scholar 

  • Lin W H, Kulkarni A, Mirchandani P (2004). Short-term arterial travel time prediction for advanced traveler information systems. Journal of Intelligent Transportation Systems, 8(3): 143–154

    Article  Google Scholar 

  • Long Cheu R, Xie C, Lee D H (2002). Probe vehicle population and sample size for arterial speed estimation. Comput Aided Civ Infrastruct Eng, 17(1): 53–60

    Article  Google Scholar 

  • Mori U, Mendiburu A, Álvarez M, Lozano J A (2015). A review of travel time estimation and forecasting for Advanced Traveller Information Systems. Transportmetrica A. Transport Sci, 11(2): 119–157

    Article  Google Scholar 

  • Nahar L, Sultana Z (2014). A new travel time prediction method for intelligent transportation system. IOSR Journal of Computer Engineering, 16(3): 24–30

    Article  Google Scholar 

  • Shalaby A, Farhan A (2003). Bus travel time prediction model for dynamic operations control and passenger information systems. Transportation Research Board, 2

    Google Scholar 

  • Stathopoulos A, Karlaftis M G (2003). A multivariate state space approach for urban traffic flow modeling and prediction. Transp Res, Part C Emerg Technol, 11(2): 121–135

    Article  Google Scholar 

  • Tu H, van Lint H W C, van Zuylen H J (2007). Impact of adverse weather on travel time variability of freeway corridors. In: Proceedings of Transportation Research Board 86th Annual Meeting

    Google Scholar 

  • van Lint J W C, Hoogendoorn S P, van Zuylen H J (2005). Accurate freeway travel time prediction with state-space neural networks under missing data. Transp Res, Part C Emerg Technol, 13(5–6): 347–369

    Article  Google Scholar 

  • Vlahogianni E I, Golias J C, Karlaftis M G (2004). Short-term traffic forecasting: overview of objectives and methods. Transp Rev, 24(5): 533–557

    Article  Google Scholar 

  • Wei L, Fang Z, Luan S (2009). Travel time prediction method for urban expressway link based on artificial neural network. In: Proceedings of 2013 International Conference on Computing, Networking and Communications (ICNC), 1: 358–362

    Google Scholar 

  • Wu C H, Ho J M, Lee D T (2004). Travel-time prediction with support vector regression. IEEE Trans Intell Transp Syst, 5(4): 276–281

    Article  Google Scholar 

  • Zhan X, Hasan S, Ukkusuri S V, Kamga C (2013). Urban link travel time estimation using large-scale taxi data with partial information. Transp Res, Part C Emerg Technol, 33(4): 37–49

    Article  Google Scholar 

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Acknowledgements

This research was sponsored by the National Natural Science Foundation of China (Grant No. 41271441).

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Authors and Affiliations

  1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China

    Tao Xu & Xiang Li

  2. School of Geographic Sciences, East China Normal University, Shanghai, 200241, China

    Tao Xu & Xiang Li

  3. The French Naval Academy Research Institute, Lanveoc-Poulmic, BP 600, 29240, Brest Naval, France

    Christophe Claramunt

Authors
  1. Tao Xu
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  2. Xiang Li
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  3. Christophe Claramunt
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Correspondence to Xiang Li.

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Xu, T., Li, X. & Claramunt, C. Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories. Front. Earth Sci. 12, 253–263 (2018). https://doi.org/10.1007/s11707-016-0634-8

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  • DOI: https://doi.org/10.1007/s11707-016-0634-8

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