Journal of Dalian Maritime University >

2019 , Vol. 45 >Issue 1: 74 - 81

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2019.01.009

Traffic demand forecasting model for container terminal based on non-parametric kernel density estimation

  • MA Meng-zhi ,
  • FAN Hou-ming ,
  • HUANG Ju-sen ,
  • KONG Liang ,
  • YUE Li-jun
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  • （Transportation Engineering College, Dalian Maritime University, Dalian 116026, China）

Received date: 2019-01-14

  Revised date: 2019-01-14

  Online published: 2019-01-25

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Abstract

To address the inadequacies associated with present parametric density estimations for containers’ delivery and pick up time distributions， a traffic demand forecasting model based on nonparametric kernel density estimation was developed．Gaussian kernel was chosen as the kernel function and the optimal window width was obtained by the crossvalidation method．The  χ2 test, K-S test and posteriori test were used to compare the goodnessoffit of the proposed probabilistic model and two conventional parametric distribution models, and the proposed model was applied to forecast the traffic demand of DCT. The results demonstrate that the proposed nonparametric estimation has better accuracy, stability and applicability. The probability density curve obtained can more accurately reflect the overall distribution pattern of containers’ delivery and pick up time. The traffic demand forecasting model based on nonparametric kernel density estimation has higher prediction accuracy than that of the conventional parametric distribution models, which can provide more accurate traffic volume and task volume prediction for the infrastructure planning of container terminal, road traffic management, allocation and scheduling of terminal resources.

Key words： container terminal; traffic demand forecasting; non-parametric kernel density estimation; cross-validation

Cite this article

MA Meng-zhi , FAN Hou-ming , HUANG Ju-sen , KONG Liang , YUE Li-jun . Traffic demand forecasting model for container terminal based on non-parametric kernel density estimation[J]. Journal of Dalian Maritime University, 2019 , 45(1) : 74 -81 . DOI: 10.16411/j.cnki.issn1006-7736.2019.01.009

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