Investigation of crowdshipping delivery trip production with real-world data

Hui Shen and Jane Lin

Transportation Research Part E: Logistics and Transportation Review, 2020, vol. 143, issue C

Abstract: Crowd-shipping (CS) is an innovative logistics service that occasional and professional couriers sign up for via an online platform to deliver packages upon requests by senders. Currently the demand and supply of CS is not yet well understood, largely due to the limited real-world data. This study aims to first fill this gap by analyzing the real-world CS data from the city of Atlanta, GA between April 2015 and August 2018. We first present an overview of the real-world CS data in three aspects: (1) the CS pricing scheme; (2) the CS spatial and temporal delivery patterns; and (3) comparison of preferences between the senders’ requests and the couriers’ bids. The analysis finds that the CS service has a clear price advantage over FedEx in the same-day and express service, as well as in the large, extra large, and huge size package delivery. The data analysis also reveals considerable discrepancies between senders’ and couriers’ preferences. We then compare two classes of the state-of-the-art Deep Learning (DL) methods in their ability to predict short-term CS delivery trip production. One class captures only the temporal features, namely the Long Short-term Memory Neural Network (LSTM), the Bidirectional Long Short-term Memory Neural Network (BDLSTM), and the Gated Recurrent Unit (GRU). The other class considers both spatial and temporal features, namely Convolutional Neural Network (CNN), CNN-LSTM, and ConvLSTM. The results show that ConvLSTM has overall the best predictive performance among the six DL methods considered, proving the importance of capturing both the spatial and temporal features of the delivery trip production data, as well as the convolutional nature of the spatial and temporal features in the data.

Keywords: Crowdshipping (CS); Delivery trip production forecasting; Spatial and temporal correlation; Discrepancies in CS demand and supply; Spatio-temporal deep learning (DL) method (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.tre.2020.102106

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