U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
202-366-4000
Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
| SUMMARY |
| This report is an archived publication and may contain dated technical, contact, and link information |
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| Publication Number: FHWA-HRT-21-089 Date: February 2022 |
Publication Number: FHWA-HRT-21-089 Date: February 2022 |
PDF Version (6.03 MB)
Technical Report Documentation Page
| 1. Report No.
FHWA-HRT-21-089 |
2. Government Accession No. | 3 Recipient's Catalog No. | ||
| 4. Title and Subtitle
Integrated Data Collection Method for Tracking Freight Movement |
5. Report Date February 2022 |
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| 6. Performing Organization Code | ||||
| 7. Author(s)
Moshe Ben-Akiva (0000-0002-9635-9987), Jinping Guan, Kyungsoo Jeong, Jing Ding-Mastera, Peiyu Jing (ORCID: 0000-0003-1179-6211), Monique Stinson, Eric Manzi, William Wong, William Ko, Ernest Guok, Dao Hieu, Christina Lui, Le Tan Thanh, Paola Garbagnoli, Fang Zhao (ORCID: 0000-0003-2800-4479), Linlin You, Vittorio Marzano |
8. Performing Organization Report No.
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9. Performing Organization Name and Address Massachusetts Institute of Technology |
10. Work Unit No. (TRAIS) |
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| 11. Contract or Grant No.
DTFH6115C00033 |
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| 12. Sponsoring Agency Name and Address
Office of Corporate Research, Technology, and Innovation Management |
13. Type of Report and Period Covered
Research Summary Report |
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14. Sponsoring Agency Code HRTM-30 |
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| 15. Supplementary Notes
The Contracting Officer's representative is Birat Pandey (HOFM-1). |
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| 16. Abstract
This research summary report describes research carried out to develop an integrated survey methodology and instrument to support the collection of freight movement data. The researchers leveraged the Future Mobility Sensing (FMS) framework, which combines tracking and sensing data with machine-learning algorithms on the back end and user participation to convert raw passive tracking data into processed and verified data. The researchers conducted case studies in each of three phases of the project to test the FMS system with vehicle tracking, shipment tracking, and an integrated test to track both vehicles and shipments together. The project served as a proof of concept that new and emerging technologies can overcome some of the existing challenges to freight logistics data collection. |
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| 17. Key Words
Freight, logistics, Future Mobility Sensing, FMS, commodity flow survey, mobile sensing, machine learning |
18. Distribution Statement
No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161. |
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19. Security Classification Unclassified |
20. Security Classification Unclassified |
21. No. of Pages 16 |
22. Price N/A |
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| Form DOT F 1700.7 (8-72) | Reproduction of completed page authorized |
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