Data-Driven Next Destination Prediction and ETA Improvement for Urban Delivery Fleets

Data-Driven Next Destination Prediction and ETA Improvement for Urban Delivery Fleets
Title:
Data-Driven Next Destination Prediction and ETA Improvement for Urban Delivery Fleets
Other Titles:
IET Intelligent Transport Systems
Publication Date:
26 July 2019
Citation:
Zhao, B., Teo, Y. S., Ng, W. S., & Ng, H. H. (2019). Data-driven next destination prediction and ETA improvement for urban delivery fleets. IET Intelligent Transport Systems.
Abstract:
In an urban logistics system, predictions of the next destination and estimated time of arrival (ETA) are of paramount importance for efficient resource planning of delivery fleets and for providing a satisfactory client experience. The quality of prediction is limited by the information accessible to individual logistics business entities, and further complicated by the complex urban road system. Data collection under the auspices of smart city initiatives worldwide provides exciting new opportunities to overcome these limitations. In this paper, we identify two areas of improvement through data-driven approaches, including a next destination predictor, based on the delivery fleet’s historical Global Positioning System (GPS) trajectory data using a nonlinear autoregressive neural network (NARNN), and a road incident detector for real-time ETA improvement. By comparing a range of machine learning classification algorithms for incident detection, XGBoost has been found to be the most practical choice, due to its performance and efficiency. The proposed framework can be utilized by government authorities who possess such data for better urban planning and providing advanced infrastructure, so as to improve the operational efficiency of the logistics industry.
License type:
PublisherCopyrights
Funding Info:
The work was partially supported by the A*STAR SERC Grant No. 1626700038 and the National Research Foundation, Prime Minister’s Office, Singapore, under NRF-NSFC Joint Research Grant Call on Data Science (NRF2016NRF-NSFC001-113).
Description:
ISSN:
1751-9578
1751-956X
Files uploaded:
File Size Format Action
There are no attached files.