Vehicle Edge Computing for Travel Behavior and Demand in Future Intelligent Transportation Systems (ITS)

Term Start:

June 1, 2024

Term End:

May 31, 2025

Budget:

$83,832

Keywords:

Edge Computing, Travel Behavior

Thrust Area(s):

Data Modeling and Analytic Tools, Equity and Understanding User Needs

University Lead:

California State Polytechnic University, Pomona

Researcher(s):

Yunsheng Wang

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. 

Project scope

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