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Research Spending & Results

Award Detail

Doing Business As Name:University of Southern California
  • Petros Ioannou
  • (213) 740-4452
  • Maged M Dessouky
  • Genevieve Giuliano
Award Date:09/16/2019
Estimated Total Award Amount: $ 1,109,871
Funds Obligated to Date: $ 1,109,871
  • FY 2019=$1,109,871
Start Date:10/01/2019
End Date:09/30/2022
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CPS: Medium Collaborative Research: Smart Freight Transport Using Behavioral Incentives
Federal Award ID Number:1932615
DUNS ID:072933393
Parent DUNS ID:072933393
Program:CPS-Cyber-Physical Systems
Program Officer:
  • David Corman
  • (703) 292-8754

Awardee Location

Street:University Park
City:Los Angeles
County:Los Angeles
Awardee Cong. District:37

Primary Place of Performance

Organization Name:University of Southern California
Street:3740 McClintock Ave EEB200
City:Los Angeles
County:Los Angeles
Cong. District:37

Abstract at Time of Award

The purpose of this project is to develop new methods to increase the efficiency of freight activity, thereby reducing air pollution and greenhouse gas emissions associated with the freight sector. There are widespread inefficiencies in the freight transport system, many due to lack of coordination across actors in the system: railroads and trucking firms, shipping companies, cargo owners, and port operators. There is a need for a centrally coordinated freight management system that will optimize the flow of freight across the rail and road transportation networks. These networks are very complicated: freight and passengers share the same infrastructure; there is temporal and spatial variation in demand; there are many decision-makers affecting the system. We use optimization and control theory and techniques combined with behavioral models to develop a centrally managed control system. The fundamental concept is efficient freight load balancing, meaning allocating freight demand across time and space to serve the entire set of demands as efficiently as possible. In order to accomplish optimal load balancing, we must address two questions: 1) What is the most efficient and sustainable spatio-temporal allocation of freight shipments across the road network, and 2) what incentives and pricing tools will motivate users to accept this allocation. The second question is necessary, because a system optimization requires changes in behavior for some users relative to each user optimizing independently. Our research approach is co-Simulation, Control and Optimization with BEhavioral incentives (SCOBE). We use simulation models as part of a closed loop optimization system where the system dynamics and user behavior are monitored and updated. Once we allow for variation in user preferences, conventional approaches to solving the system optimization problem are inadequate. Rather, we must take user preferences directly into account in order to determine the best combination of shipment allocations and user incentives. This project makes the following contributions: First, it advances science by developing a method for system optimization with variation in user preferences. Second, the resulting method will be demonstrated through the participation of at least one trucking company to evaluate its value in a real-world context. Third, particulate emissions from heavy duty trucks are one of the largest sources of human health impacts in urbanized areas. Increasing freight efficiency will reduce particulate and other emissions, as well as reduce greenhouse gas emissions. Fourth, the research will be used to promote under-represented undergraduate students to pursue graduate studies, and promote under-represented high school students to pursue careers in the science, technology, engineering and math (STEM) fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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