J Shanghai Jiaotong Univ Sci ›› 2024, Vol. 29 ›› Issue (5): 801-808.doi: 10.1007/s12204-022-2415-8

• Naval Architecture, Ocean and Civil Engineering • Previous Articles     Next Articles

Predictive Simulation of External Truck Operation Time in a Container Terminal Based on Traffic Big Data

基于交通大数据的集装箱码头外集卡作业时长仿真预测

DU Ye1 (杜晔), ZHAO Yifei2* (赵一飞), GAO Deyi1 (高德毅)   

  1. (1. Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China; 2. Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China)
  2. (1.上海海事大学 商船学院,上海 201306;2. 上海交通大学 安泰经济与管理学院,上海 200030)
  • Accepted:2021-09-14 Online:2024-09-28 Published:2024-09-28

Abstract: The operation time of external trucks in a container terminal is one of port operation key performance indicators concerned by port operators, external truck operators and related government authorities. With the traffic big data combined with the operation characteristics of the container terminal, the system dynamics method is used to build the simulation model of the operation system for external trucks. The simulation results of the operation time of external trucks are consistent with the actual situation, which provides an effective way to eliminate the “black box” of the operation time of the external trucks. The model can also be applied in multiple scenarios by using the traffic big data, and the simulation results can be adopted by the relevant organizations.

Key words: big data, external truck, operation time, system dynamics, predictive simulation

摘要: 集装箱码头外集卡作业时长是港口运营方、外集卡运营商和相关政府机构共同关注的港口运营关键绩效指标之一。通过交通大数据结合集装箱码头的运营特点,采用了系统动力学方法建立外集卡作业时长的系统动力学模型。该模型对外集卡作业时长的仿真结果与实际数据具有一致性,提供了一种有效方式来消除外集卡作业时长的“黑箱”情况。该模型可利用交通大数据在多种作业场景下进行应用,模型的仿真结果可用于相关机构作为预测数据。

关键词: 大数据,外集卡,作业时长,系统动力学,仿真预测

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