Please wait a minute...

Frontiers of Earth Science

Front. Earth Sci.    2018, Vol. 12 Issue (2) : 253-263     https://doi.org/10.1007/s11707-016-0634-8
RESEARCH ARTICLE |
Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories
Tao XU1,2, Xiang LI1,2(), Christophe CLARAMUNT3
1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
2. School of Geographic Sciences, East China Normal University, Shanghai 200241, China
3. The French Naval Academy Research Institute, Lanveoc-Poulmic, BP 600, 29240 Brest Naval, France
Download: PDF(1973 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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.

Keywords trip-oriented travel time prediction (TOTTP)      urban network      Back Propagation Neural Networks (BPNN)      historical vehicle trajectories     
Corresponding Authors: Xiang LI   
Just Accepted Date: 07 December 2016   Online First Date: 17 March 2017    Issue Date: 09 May 2018
 Cite this article:   
Tao XU,Xiang LI,Christophe CLARAMUNT. Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories[J]. Front. Earth Sci., 2018, 12(2): 253-263.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-016-0634-8
http://journal.hep.com.cn/fesci/EN/Y2018/V12/I2/253
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Tao XU
Xiang LI
Christophe CLARAMUNT
Fig.1  Overview of the methodology.
Weather condition Value
Sunny
Cloudy
1
Drizzle
Sleet
Light Snow
2
Moderate Rain 3
Heavy Rain
Rainstorm
4
Moderate Snow
Heavy Snow
Blizzard
Other
5
Tab.1  Quantified weather conditions
AQI levels Value
Excellent 1
Good 2
Light pollution 3
Moderate pollution 4
Severe pollution 5
Serious pollution 6
Tab.2  Quantified AQI
Fig.2  Structure of BPNN model.
Fig.3  Road network and 3 selected traffic zones.
Origin and destination Route No. Number of trips Route length/km
From Zone Bto Zone A Route 1 5206 14.36
Route 2 2751 11.91
Route 3 273 18.87
From Zone C to Zone A Route 4 205 26.95
Route 5 169 23.78
Route 6 45 29.08
Tab.3  Trip and route information
Fig.4  Selected routes (a) from Zone B to Zone A and (b) from Zone C to Zone A.
Fig.5  The relationship between training error E and different values of h or a when a or h is fixed during a training process for Route 4.
Fig.6  Predicted travel time for trips in June 2014 from Zone B to Zone A.
Fig.7  Predicted travel time for trips in June 2014 from Zone C to Zone A.
Route n l/km The proposed method Moving average method
E^/min R E^/min R
1 218 14.36 3.64 0.253 5.28 0.368
2 420 11.91 3.28 0.275 4.03 0.338
3 27 18.87 9.87 0.523 14.31 0.758
4 9 26.95 3.03 0.112 9.59 0.356
5 16 23.78 11.47 0.482 13.21 0.556
6 4 29.08 8.63 0.297 19.98 0.687
Tab.4  Error comparison between moving average method and the proposed method
Fig.8  Error analysis by hour for trips along Route 2 on the first day of 5 continuous workdays.
Input factors* E^
DTWAC 3.28
DTAC 4.08
DTWA 4.11
DTWC 4.32
DT 6.23
Tab.5  Meteorological factor analysis
1 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
https://doi.org/10.1016/j.sbspro.2011.04.443
2 Alecsandru C, Ishak S (2004). Hybrid model-based and memory-based traffic prediction system. Transp Res Rec, 1879(1): 59–70
https://doi.org/10.3141/1879-08
3 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
https://doi.org/10.1016/j.trc.2007.06.003
4 Bhaskar A, Chung E, Dumont A (2011). Arterial travel time estimation: revisiting the classical procedure. Australasian Transport Research Forum Adelaide South Australia, 34
5 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
6 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
https://doi.org/10.3141/1768-19
7 Chien S I, Kuchipudi C M (2003). Dynamic travel time prediction with real-time and historic data. J Transp Eng, 129(6): 608–616
https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(608)
8 Dia H (2001). An object-oriented neural network approach to short-term traffic forecasting. Eur J Oper Res, 131(2): 253–261
https://doi.org/10.1016/S0377-2217(00)00125-9
9 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
https://doi.org/10.1016/j.trc.2014.02.014
10 Dougherty M (1995). A review of neural networks applied to transport. Transp Res, Part C Emerg Technol, 3(4): 247–260
https://doi.org/10.1016/0968-090X(95)00009-8
11 Dougherty M S, Cobbett M R (1997). Short-term inter-urban traffic forecasts using neural networks. Int J Forecast, 13(1): 21–31
https://doi.org/10.1016/S0169-2070(96)00697-8
12 Elabd S, Schlenkhoff A (2009). ANFIS and BP neural network for travel time prediction. World Acad Sci Eng Technol, 57: 116–121 doi: 10.1.1.193.3167
13 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
https://doi.org/10.1016/j.trc.2010.10.005
14 Goh A T C (1995). Back-propagation neural networks for modeling complex systems. Artif Intell Eng, 9(3): 143–151
https://doi.org/10.1016/0954-1810(94)00011-S
15 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
https://doi.org/10.1016/j.trc.2014.02.006
16 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
https://doi.org/10.1016/j.trc.2009.10.006
17 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
https://doi.org/10.1016/j.trb.2012.03.006
18 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
19 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
https://doi.org/10.1061/(ASCE)0733-947X(2004)130:4(452)
20 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
https://doi.org/10.1016/j.trb.2013.03.008
21 Jiang Y J, Li X (2013). Travel time prediction based on historical trajectory data. Ann GIS, 19(1): 27–35
https://doi.org/10.1080/19475683.2012.758173
22 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
https://doi.org/10.1109/ITSC.2013.6728483
23 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
24 Kůrková V (1992). Kolmogorov’s theorem and multilayer neural networks. Neural Netw, 5(3): 501–506
https://doi.org/10.1016/0893-6080(92)90012-8
25 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
https://doi.org/10.1016/j.eswa.2008.03.018
26 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
https://doi.org/10.1109/PERCOMW.2011.5766967
27 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
https://doi.org/10.1080/15472450490492833
28 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
https://doi.org/10.1111/1467-8667.00252
29 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
30 Nahar L, Sultana Z (2014). A new travel time prediction method for intelligent transportation system. IOSR Journal of Computer Engineering, 16(3): 24–30
https://doi.org/10.9790/0661-16382430
31 Shalaby A, Farhan A (2003). Bus travel time prediction model for dynamic operations control and passenger information systems. Transportation Research Board, 2
32 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
https://doi.org/10.1016/S0968-090X(03)00004-4
33 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
35 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
https://doi.org/10.1016/j.trc.2005.03.001
36 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
https://doi.org/10.1080/0144164042000195072
37 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
38 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
https://doi.org/10.1109/TITS.2004.837813
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed