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Link to original content: https://doi.org/10.1007/s12559-018-9603-8
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Travel Time Functions Prediction for Time-Dependent Networks

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Abstract

The studies on the TDN (time-dependent network), in which the travel time of the same road segment varies depending on the time of the day, have attracted much attention of researchers, but there is little work focusing on the travel time functions prediction problem. Though traditional methods for travel time or travel speed prediction problem can be used to generate the travel time functions, they have some limitations due to the need of less breakpoints, fine granularity, and long-term prediction. In this paper, we study the travel time functions prediction problem for TDN based on taxi trajectory data. In order to maintain a high degree of accuracy in fine-grained and long-predicted situations, we take into account not only the traffic incidents but also the data sparsity. Specifically, a traffic incident detection method is proposed based on k-means algorithm and a downstream-based strategy is proposed to estimate the speeds of segments considering the data sparsity. To make the breakpoints of function not so much, a prediction algorithm based on classification using ELM (extreme learning machine) is proposed, which predicts the speed classes taking both the weather and the adjacent segment conditions into account. In addition, a transformation method is presented to convert the discrete travel speeds into piecewise linear functions satisfying FIFO (First-In-First-Out) property. The experimental results show that ELM outperforms SVM (support vector machine) with regard to both the training time and prediction accuracy. Moreover, it also can be seen that both the weather conditions and the adjacent segment conditions have impact on the prediction accuracy.

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Notes

  1. http://www.datatang.com/data/44502

  2. http://lishi.tianqi.com/beijing/201211.html

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Funding

This research was partially supported by the National Natural Science Foundation of China under Grant No. 61502317; and the Natural Science Foundation of Liaoning Province under Grant No.201602559; and the Natural Science Foundation of Liaoning Province under Grant 201602568; and the National Natural Science Foundation of China under Grant No. 61701322, 61502316.

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Authors and Affiliations

  1. School of Computer Science, Shenyang Aerospace University, Shenyang, China

    Jiajia Li, Xiufeng Xia, Xiangyu Liu & Liang Zhao

  2. School of Computer Science, Northeastern University, Shenyang, China

    Botao Wang

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  1. Jiajia Li
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  2. Xiufeng Xia
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  3. Xiangyu Liu
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  4. Liang Zhao
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Correspondence to Jiajia Li or Xiufeng Xia.

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Li, J., Xia, X., Liu, X. et al. Travel Time Functions Prediction for Time-Dependent Networks. Cogn Comput 11, 145–158 (2019). https://doi.org/10.1007/s12559-018-9603-8

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