A benchmark for predicting turnaround time for trucks at a container terminal

Sjoerd van der Spoel, Chintan Amrit, Jos van Hillegersberg

Research output: Contribution to conferencePaperAcademic

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Abstract

Creating a reliable predictive model is a vital part of business intelligence applications. However, without a proper benchmark, it is very difficult to assess how good a predictive model really is. Furthermore, existing literature does not provide much guidance on how to create such benchmarks. In this paper we address this gap by presenting a method for creating such a such a benchmark. We demonstrate the method by developing predictive models for truck turnaround time, created using both regression and classification methods. We use data generated in a simulated terminal for developing these models. We establish the parameters and parameter distributions of the simulation through a structured review of the relevant literature. We show that congestion, start time and route through the terminal together are good predictors of turnaround time, leading to adequate predictive performance. These results can then be used as a benchmark for predictive models on truck turnaround time, thereby demonstrating our general method for creating such benchmarks.
Original languageEnglish
Number of pages10
Publication statusPublished - 29 Mar 2016
EventBig Data Interoperability for Enterprises (BDI4E) Workshop 2016 - Guimarães, Portugal
Duration: 29 Mar 201630 Mar 2016

Conference

ConferenceBig Data Interoperability for Enterprises (BDI4E) Workshop 2016
Abbreviated titleBDI4E
CountryPortugal
CityGuimarães
Period29/03/1630/03/16

Fingerprint

Turnaround time
Trucks
Containers
Competitive intelligence

Keywords

  • IR-102150
  • METIS-318959

Cite this

van der Spoel, S., Amrit, C., & van Hillegersberg, J. (2016). A benchmark for predicting turnaround time for trucks at a container terminal. Paper presented at Big Data Interoperability for Enterprises (BDI4E) Workshop 2016, Guimarães, Portugal.
van der Spoel, Sjoerd ; Amrit, Chintan ; van Hillegersberg, Jos. / A benchmark for predicting turnaround time for trucks at a container terminal. Paper presented at Big Data Interoperability for Enterprises (BDI4E) Workshop 2016, Guimarães, Portugal.10 p.
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van der Spoel, S, Amrit, C & van Hillegersberg, J 2016, 'A benchmark for predicting turnaround time for trucks at a container terminal' Paper presented at Big Data Interoperability for Enterprises (BDI4E) Workshop 2016, Guimarães, Portugal, 29/03/16 - 30/03/16, .

A benchmark for predicting turnaround time for trucks at a container terminal. / van der Spoel, Sjoerd; Amrit, Chintan; van Hillegersberg, Jos.

2016. Paper presented at Big Data Interoperability for Enterprises (BDI4E) Workshop 2016, Guimarães, Portugal.

Research output: Contribution to conferencePaperAcademic

TY - CONF

T1 - A benchmark for predicting turnaround time for trucks at a container terminal

AU - van der Spoel, Sjoerd

AU - Amrit, Chintan

AU - van Hillegersberg, Jos

PY - 2016/3/29

Y1 - 2016/3/29

N2 - Creating a reliable predictive model is a vital part of business intelligence applications. However, without a proper benchmark, it is very difficult to assess how good a predictive model really is. Furthermore, existing literature does not provide much guidance on how to create such benchmarks. In this paper we address this gap by presenting a method for creating such a such a benchmark. We demonstrate the method by developing predictive models for truck turnaround time, created using both regression and classification methods. We use data generated in a simulated terminal for developing these models. We establish the parameters and parameter distributions of the simulation through a structured review of the relevant literature. We show that congestion, start time and route through the terminal together are good predictors of turnaround time, leading to adequate predictive performance. These results can then be used as a benchmark for predictive models on truck turnaround time, thereby demonstrating our general method for creating such benchmarks.

AB - Creating a reliable predictive model is a vital part of business intelligence applications. However, without a proper benchmark, it is very difficult to assess how good a predictive model really is. Furthermore, existing literature does not provide much guidance on how to create such benchmarks. In this paper we address this gap by presenting a method for creating such a such a benchmark. We demonstrate the method by developing predictive models for truck turnaround time, created using both regression and classification methods. We use data generated in a simulated terminal for developing these models. We establish the parameters and parameter distributions of the simulation through a structured review of the relevant literature. We show that congestion, start time and route through the terminal together are good predictors of turnaround time, leading to adequate predictive performance. These results can then be used as a benchmark for predictive models on truck turnaround time, thereby demonstrating our general method for creating such benchmarks.

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KW - METIS-318959

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van der Spoel S, Amrit C, van Hillegersberg J. A benchmark for predicting turnaround time for trucks at a container terminal. 2016. Paper presented at Big Data Interoperability for Enterprises (BDI4E) Workshop 2016, Guimarães, Portugal.