Integrated Data Collection Method for Tracking Freight Movement

Ben-Akiva, Moshe; Guan, Jinping; Jeong, Kyungsoo; Ding-Mastera, Jing; Jing, Peiyu; Stinson, Monique; Manzi, Eric; Wong, William; Ko, William; Guok, Ernest; Hieu, Dao; Lui, Christina; Thanh, Le Tan; Garbagnoli, Paola; Zhao, Fang; You, Linlin; Marzano, Vittorio; Massachusetts Institute of Technology; Schatz Publishing Group

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Integrated Data Collection Method for Tracking Freight Movement

2022-02-01
By Ben-Akiva, Moshe ; Guan, Jinping ; Jeong, Kyungsoo ; ...

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English

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Details:

Creators:

Ben-Akiva, Moshe ; Guan, Jinping ; Jeong, Kyungsoo ; Ding-Mastera, Jing ; Jing, Peiyu ; ... More +

Ben-Akiva, Moshe ; Guan, Jinping ; Jeong, Kyungsoo ; Ding-Mastera, Jing ; Jing, Peiyu ; Stinson, Monique ; Manzi, Eric ; Wong, William ; Ko, William ; Guok, Ernest ; Hieu, Dao ; Lui, Christina ; Thanh, Le Tan ; Garbagnoli, Paola ; Zhao, Fang ; You, Linlin ; Marzano, Vittorio Less -
Corporate Creators:

Massachusetts Institute of Technology ; Schatz Publishing Group

Massachusetts Institute of Technology ; Schatz Publishing Group Less -
Contributors:

Pandey, Birat
Corporate Contributors:

United States. Federal Highway Administration. Office of Corporate Research, Technology, and Innovation Management
Subject/TRT Terms:

[+]

Commodity Flow Commodity Flow Survey FMS Freight Freight Transportation Future Mobility Sensing Logistics Machine Learning Mobile Sensing Shared Mobility Traffic Surveys
Publication/ Report Number:

FHWA-HRT-21-089
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Research Summary Report
Contracting Officer:

Pandey, Birat
Corporate Publisher:

United States. Federal Highway Administration. Office of Corporate Research, Technology, and Innovation Management
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.
Format:

PDF
Funding:

DTFH6115C00033
Collection(s):

Federal Highway Administration
Main Document Checksum:

[+]

urn:sha-512:772dee41193eedb5446f13a53eba64585c20c8c0fba8976314fe73417b6a7b0e95bb6e1cf8076b11ab99c0c59cd0eaf81354e02f61eee9839c334dceddf2a5c7
Download URL:

https://rosap.ntl.bts.gov/view/dot/62441/dot_62441_DS1.pdf
File Type:

[PDF-6.03 MB]