This file elaborates how drayage (truck) operators can apply predictive analytic techniques to their internal data assets to extract better insights and improve their operational decision making. For more detail please find our Decision Support Systems journal article at http://dx.doi.org/10.1016/j.dss.2016.11.008

1. DEPARTMENT OF TECHNOLOGY & OPERATIONS MANAGEMENT
The Seaport Service Rate
Prediction System:
Using Drayage Truck Trajectory Data to
Predict Seaport Service Rates
authors:
Meditya Wasesa, Andries Stam, Eric van Heck
Wasesa, M., Stam, A. & van Heck, E., 2017. The Seaport Service Rate Prediction System: Using Drayage
Truck Trajectory Data to Predict Seaport Service Rates. Decision Support Systems, 95(1), pp.37–48.

2. Overview
Central Question
“How drayage operators can apply predictive analytic techniques to their internal data
assets to extract better insights and improve their operational decision making?”
Article Highlights
• This study presents a seaport service rate prediction system that will help drayage
operators to predict the duration of the pick-up/delivery operation at a seaport by using
their subordinate trucks’ trajectory data.
• With the proposed approach, the seaport service rate can be estimated without modifying
the existing design of the seaports appointment system.
• The seaport service rate prediction system is constructed based on three components
namely, trajectory reconstruction, geo-fencing analysis, and gradient boosting modelling.
• Using predictive analytic techniques, the prediction system is trained and validated using
more than 15 million data records from over 200 trucks over a period of 19 months.
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3. Background – The Business Context
Negative impacts of congestion near seaport area
• Resource allocation balancing problems for the Seaports
• Unproductive waiting time for the drayage trucks at over-utilized ports.
• Queues of trucks increasing road congestion and generating pollution, etc.
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Each congestion mitigation initiative has its challenge
• Road (traffic) Diversion Initiatives [High Financial Capital]
– Dry ports
– Extended gate
• Non Diversion initiatives [Drayage Operators’ Low Participation]
– Extending the seaport’s opening hours
– Seaport’s appointment system

4. Background – The Literatures Gap
• The application of predictive analytics to novel data sources (from sensor to
social media data) has received increasing interest.
• Developing predictive analytics to support operational decision making is still
an under-researched field.
• To circumvent the need to modify the appointment system's design (their
existing inter-organizational system), companies can explore the opportunity to
use their internal data assets to extract better insights and improve their
operational decision making using predictive analytics.
• This approach has not been applied in any research initiative aiming to improve
the container pick-up/delivery operations, especially the ones that correlated
with the appointment system initiatives.
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Our system uses using sensor-
based trajectory dataset that is
updated every few minutes.

5. Research Positioning
• We approach the problem from the drayage operators‘ perspective
that seeks to minimize the loading/unloading time at seaports.
• We consider the drayage operators' wealth of trajectory (GPS)
data, mined from the subordinate trucks' telematics system, as a
valuable resource for evaluating a seaport's service rate.
• The general theme is defined as follows: “How drayage operators
can apply predictive analytic techniques to their data assets to
extract better insights and improve their operational decision
making.”
• The objective is to present a seaport service rate prediction
system that uses the trajectory data communicated through the
drayage trucks' telematics devices, i.e. the board computer.
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6. The Seaport Service Rate Prediction System
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The Framework

7. The Data Specification
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8. Method (1): Trajectory Reconstruction & Geo-fencing Analysis
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• The trajectory
reconstruction -> plots
the trucks' historical
position on a map
• The geo-fencing
technique -> measure
the duration the truck
remained in the
reviewed seaport

9. Method (2): The Prediction Model Formulation
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We opt for the gradient boosting model

10. Result (1): Trajectory Reconstruction
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11. Result (2): Geo-fencing Analysis
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12. Result (3): Prediction Model
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The addition of inertia effects significantly improve the prediction results.

13. Result (3): Prediction Model
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14. Conclusion (1)
Main Conclusion
• With the seaport service rate prediction system, we provide a solution for predicting
seaport service rate performance using drayage trucks' trajectory data.
• Using a high volume of data with modest information features, data re-engineering efforts
are necessary to increase the predictive power of the model (in this case temporal and
inertia effects are derived from the existing dataset and treat them as main predictors.
• The proposed gradient-boosting model-based solution provides better predictions than
the benchmark solution (the linear model).
Practical Implications
• The system can support drayage operators in predicting a seaport's service rate so that
truck route planning will minimize the time spent at seaports (stop points).
• The system can also be applied to predict any service station's service rate.
• In generating predictions, the system uses the vehicle telematics data logs (trajectory
(GPS) data), circumventing the need to modify the existing appointment system.
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15. Conclusion (2)
Contribution to Literatures
• This design science study [63,64] corresponds to the largely unexplored field of predictive
analytics development using geospatial sensor-based data [19,20,22].
• This study offers a new approach to the seaport congestion issue and explains how
stakeholders can use predictive analytics techniques on their data assets to extract better
insights to improve their decision making [15,27,51,56,58,62].
• To the best of our knowledge, this approach has not been introduced in the research
literature on seaport diversion initiatives [7–10], non-diversion initiatives [7,11–13], and
decision support systems on seaport hinterland operation topics [30–32,35–37,40,41].
• While previous studies attempted to predict the seaports' productivity for the seaside
using yearly or monthly statistics archives [43–47], this study focuses on the seaports'
landside productivity using sensor-based trajectory dataset that is updated every few
minutes.
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