NDLI: Data-driven next destination prediction and ETA improvement for urban delivery fleets

Content Provider	IET Digital Library
Author	Zhao, Bing Teo, Yon Shin Ng, Wee Siong Ng, Hai Heng
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 study, the authors identify two areas of improvement through data-driven approaches, including a next destination predictor, based on the delivery fleet's historical global positioning system trajectory data using a non-linear autoregressive neural network, 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 utilised 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.
Starting Page	1624
Ending Page	1635
Page Count	12
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 11, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0148
Journal	IET Intelligent Transport Systems
Publisher Date	2019-07-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Complex Urban Road System Data Collection Data-driven Approach Delivery Fleet Destination Predictor Knowledge Engineering Technique Learning in AI Logistics Logistics Business Entities Logistics Industry Neural Computing Technique Neural Nets Nonlinear Autoregressive Neural Network Operational Efficiency Production Engineering Computing Production Management Real-time ETA Improvement Road Incident Detector Road Traffic Smart City Initiatives Traffic Engineering Computing Urban Delivery Fleets Urban Logistics System Urban Planning
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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