A GPS-based user-verified shipment survey method to supplement the commodity flow survey: survey design, platform, and case study

Peiyu Jing, Jinping Guan (), Kyungsoo Jeong, Linlin You, Lynette Cheah, Fang Zhao and Moshe Ben-Akiva
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Peiyu Jing: Harbin Institute of Technology (Shenzhen)
Jinping Guan: Harbin Institute of Technology (Shenzhen)
Kyungsoo Jeong: Massachusetts Institute of Technology
Linlin You: Sun Yat-sen University
Lynette Cheah: Singapore University of Technology and Design
Fang Zhao: Singapore-MIT Alliance for Research and Technology Centre
Moshe Ben-Akiva: Massachusetts Institute of Technology

Transportation, 2025, vol. 52, issue 3, No 8, 923-954

Abstract: Abstract Conventional shipment data collection methods are limited due to intense labor, and lack of details on shipment paths and stops. In this view, we develop an innovative shipment survey methodology using Future Mobility Sensing (FMS)—Freight to collect shipment data at path-based origin–destination level and minimize respondent burden. FMS—Freight is a freight data collection, processing, and visualization platform which leverages sensing technologies and machine learning algorithms to interpret sensing data into travel diaries. We customized the existing FMS—Freight to accommodate the shipment survey. Specifically, we refined the stop detection, mode detection, and activity inference algorithms, revamped user interfaces, and developed a shipment data analysis and visualization tool. This web-based survey first collects the establishment’s business information, outgoing shipment information, historical shipment logs, and then requests tracking shipments with GPS devices, supplemented by a shipment registration survey and verification of shipment travel diaries. For proof-of-concept, we conducted a pilot shipment survey. Six establishments participated in the pilot and we gathered verified GPS data from 57 shipment trips. The pilot demonstrated the effectiveness of the survey design and instrument. This shipment survey has three aspects of significance: (1) It supplements the Commodity Flow Survey and enhances the capabilities to capture freight flows by combining user-verified geolocation data and detailed shipment information; (2) Collected shipment data can fill the significant data gap in the freight planning and management sector; (3) For individual establishments, FMS-Freight enables managing shipments in real-time and provides insights to assist decision-making.

Keywords: Shipment survey; GPS tracking; Machine learning; Stop detection algorithm; Mode detection algorithm; Activity inference algorithm; Commodity flow survey (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11116-023-10444-7

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