Vehicle Travel Time Estimation Using Sequence Prediction

Keywords: travel time estimation, recurrent neural networks, sequence prediction, intelligent transportation systems

Abstract

This paper proposes a region-based travel time and traffic speed prediction method using sequence prediction. Floating Car Data collected from 8,317 vehicles during 34 days are used for evaluation purposes. Twelve districts are chosen and the spatio-temporal non-linear relations are learned with Recurrent Neural Networks. Time estimation of the total trip is solved by travel time estimation of the divided sub-trips, which are constituted between two consecutive GNSS measurement data. The travel time and final speed of sub-trips are learned with Long Short-term Memory cells using sequence prediction. A sequence is defined by including the day of the week meta-information, dynamic information about vehicle route start and end positions, and average travel speed of the road segment that has been traversed by the vehicle. The final travel time is estimated for this sequence. The sequence-based prediction shows promising results, outperforms function mapping and non-parametric linear velocity change based methods in terms of root-mean-square error and mean absolute error metrics.

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Gültekin Gündüz, Sabanci University, Istanbul, Turkey

Gultekin Gündüz  received the B.Sc. degree from the Department of Computer Science and Engineering, Sabanci University, Istanbul, Turkey, in 2014, and is currently working toward the M.Sc. degree in computer engineering at Galatasaray University, Istanbul. His research interests include advanced driver assistance systems, driver behavior modeling, and machine learning.

Tankut Acarman, Faculty of Engineering and Technology, Galatasaray University, Istanbul, Turkey

Tankut Acarman received the Ph.D. degree in electrical and computer engineering from Ohio State University, Columbus, OH, USA, in 2002. He is a Professor and the Head of the Department of Computer Engineering, Galatasaray University, Istanbul, Turkey. He is a coauthor of the book titled Autonomous Ground Vehicles. His research interests include aspects of intelligent vehicle technologies, driver assistance systems, and performance evaluation of intervehicle communication.

References

Ma X, Tao Z, Wang Y, Yu H, Wang Y. Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data. Transportation Research Part C: Emerging Technologies. 2015;54: 187-197.

Kim S, Coifman B. Comparing Inrix Speed Data Against Concurrent Loop Detector Stations Over Several Months. Transportation Research Part C: Emerging Technologies. 2014;49: 59-72.

Coifman B. Empirical Flow-Density and Speed-Spacing Relationships: Evidence of Vehicle Length Dependency. Transportation Research Part B: Methodological. 2015;78: 54-65.

Yuan Y, Van Lint H, Van Wageningen-Kessels F, Hoogen-doorn S. Network-Wide Traffic State Estimation Using Loop Detector and Floating Car Data. Journal of Intelligent Transportation Systems. 2014;18(1): 41-50.

Yao B, Chen C, Cao Q, Jin L, Zhang M, Zhu H, Yu B. Short-Term Traffic Speed Prediction For An Urban

Corridor. Computer-Aided Civil and Infrastructure Engineering. 2017;32(2): 154-169.

Woodard D, Nogin G, Koch P, Racz D, Goldszmidt M, Horvitz E. Predicting Travel Time Reliability Using Mobile Phone GPS Data. Transportation Research Part C: Emerging Technologies. 2017;75: 30-44.

Zhang Y, Haghani A. A Gradient Boosting Method to Improve Travel Time Prediction. Transportation Research Part C: Emerging Technologies. 2015;58: 308-324.

Min W, Wynter L. Real-Time Road Traffic Prediction With Spatio-Temporal Correlations. Transportation Research Part C: Emerging Technologies. 2011;19: 606-616.

Ma X, Dai Z, He Z, Ma J, Wang Y, Wang Y. Learning Traffic As Images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors. 2017;17(4): 818. Available from: doi:10.3390/s17040818 [Accessed 18th September 2019].

Fei X, Lu C-C, Liu K. A Bayesian Dynamic Linear Model Approach for Real-Time Short-Term Freeway Travel Time Prediction. Transportation Research Part C: Emerging Technologies. 2011;19(6): 1306-1318.

Xia J, Chen M, Huang W. A Multistep Corridor Travel-Time Prediction Method Using Presence-Type

Vehicle Detector Data. Journal of Intelligent Transportation Systems. 2011;15(2): 104-113.

Zhang Y, Ge H. Freeway Travel Time Prediction Using Takagi–Sugeno–Kang Fuzzy Neural Network. Computer-Aided Civil and Infrastructure Engineering. 2013;28(8): 594-603.

Rahmani M, Jenelius E, Koutsopoulos HN. Non-Parametric Estimation of Route Travel Time Distributions From Low-Frequency Floating Car Data. Transportation Research Part C: Emerging Technologies. 2015;58: 343-362.

Wang Y, Zheng Y, Xue Y. Travel Time Estimation of A Path Using Sparse Trajectories. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 24-27 August 2014, New York, NY, USA. New York, NY, USA: ACM; 2014. p. 25-34.

Khosravi A, Mazloumi E, Nahavandi S, Creighton D, Van Lint J. Prediction Intervals To Account for Uncertainties in Travel Time Prediction. IEEE Transactions on Intelligent Transportation Systems. 2011;12(2): 537-547.

Bachmann C, Abdulhai B, Roorda MJ, Moshiri B. A Comparative Assessment of Multi-Sensor Data Fusion Techniques for Freeway Traffic Speed Estimation Using Microsimulation Modeling. Transportation Research Part C: Emerging Technologies. 2013;26: 33-48.

Soriguera F, Robuste F. Requiem for Freeway Travel Time Estimation Methods Based on Blind Speed Interpolations Between Point Measurements. IEEE Transactions on Intelligent Transportation Systems. 2011;12(1): 291-297.

Jenelius E, Koutsopoulos HN. Probe Vehicle Data Sampled by Time or Space: Consistent Travel Time Allocation and Estimation. Transportation Research Part B: Methodological. 2015;71: 120-137.

LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature. 2015;521(7553): 436-444.

Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation. 1997;9(8): 1735-1780.

Malhotra P, Vig L, Shroff G, Agarwal P. Long Short Term Memory Networks for Anomaly Detection in Time Series. In: Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 22-24 April 2015, Bruges, Belgium. Louvain-la-Neuve, Belgium: Presses universitaires de Louvain; 2015. p. 89-94.

Alahi A, Goel K, Ramanathan V, Robicquet A, Fei-Fei L, Savarese S. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 21-23 June 1994, Seattle, WA, USA. New York, NY, USA: IEEE; 2016. p. 961-971.

Sutskever I, Vinyals O, Le Q. V. Sequence to sequence learning with neural networks. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ. (eds.) Advances in Neural Information Processing Systems 27. Red Hook, NY USA: Curran Associates, Inc.; 2014. p. 3104-3112.

Willmott CJ, Matsuura K. Advantages of the Mean Absolute Error (MAE) Over the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research. 2005;30(1): 79-82.

Published
2020-01-20
How to Cite
1.
Gündüz G, Acarman T. Vehicle Travel Time Estimation Using Sequence Prediction. Promet [Internet]. 2020Jan.20 [cited 2024Apr.25];32(1):1-12. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3008
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Articles