Vehicle Edge Computing for Travel Behavior and Demand in Future Intelligent Transportation Systems (ITS)
Meeting the diverse needs of stakeholders such as passengers, drivers, and service providers is imperative. Modern travelers seek real-time updates and personalized journey experiences. Drivers need consolidated data for safety and punctuality (Chen et al., 2021), while service providers rely on data analytics to optimize resources and enhance reliability (Wang et al., 2020). Traditional centralized computing infrastructures struggle with the agility and responsiveness needed in the dynamic transportation landscape (Li et al., 2017). Edge computing emerges as a transformative solution by offloading computational tasks to roadside units. This enables swift processing for real-time applications, facilitating dynamic route optimization, congestion management, and resource allocation, thereby enhancing operational efficiency and reducing travel times. The project will investigate how edge computing impacts travel behavior. Field studies and simulations will measure travelers’ responsiveness to real-time data and how it influences their travel choices and demand patterns. This ensures the research is relevant to travel behavior studies. Edge computing not only enhances current transportation operations but is also crucial for autonomous vehicles. It allows real-time data processing and analysis for navigation, hazard detection, and collision avoidance. By leveraging edge computing, autonomous vehicles can offload computational tasks, alleviating the burden on onboard systems and ensuring seamless, responsive data processing without compromising safety or performance. The collaborative framework between autonomous vehicles and roadside units facilitates continuous learning and adaptation. Real-time access to advanced computing enables autonomous vehicles to use machine learning for predictive analysis, enhancing their ability to anticipate and respond to changing road conditions and traffic patterns. Integrating edge computing with autonomous vehicles creates a symbiotic relationship that enhances autonomous driving systems and accelerates the development of safer, more efficient transportation systems. This aligns the project with the theme of improving the mobility of people and goods, fitting the TBD center’s priorities.
Language
- English
Project
- Status: Active
- Funding: $167,664.00
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Contract Numbers:
69A3552344815
69A3552348320
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
Center for Understanding Future of Travel Behavior and Demand
University of Texas
Austin, TX United States -
Project Managers:
Bhat, Chandra
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Performing Organizations:
California State Polytechnic University, Pomona
3801 West Temple Avenue
Pomona, CA United States 91768 -
Principal Investigators:
Wang, Yunsheng
- Start Date: 20240601
- Expected Completion Date: 20250531
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Intelligent transportation systems; Machine learning; Real time data processing; Travel behavior; Travel demand
- Subject Areas: Data and Information Technology; Planning and Forecasting; Safety and Human Factors; Transportation (General); Vehicles and Equipment;
Filing Info
- Accession Number: 01954938
- Record Type: Research project
- Source Agency: Center for Understanding Future of Travel Behavior and Demand
- Contract Numbers: 69A3552344815, 69A3552348320
- Files: UTC, RIP
- Created Date: May 13 2025 7:05PM