TraInX: Traffic Influence Transfer Framework for Lane-Free Traffic

Abstract: Connected and autonomous vehicles and vehicle-to-everything (V2X) communication enable new forms of coordinated traffic operation, particularly in lane-free traffic (LFT), where vehicles are not constrained to predefined lanes. However, conventional interaction models mainly capture local vehicle interactions and provide limited insight into how a vehicle action influences others beyond its immediate neighborhood. This seminar presents TraInX (Traffic Influence Transfer), a framework that models direct and indirect vehicle interactions through multi-hop Influence Transfer Graphs (ITGs). TraInX organizes vehicle interactions into influence pathways and develops branch-wise dynamics incorporating velocity consensus, repulsion, and rear-induced nudging. The resulting multi-vehicle dynamics are represented using graph Laplacians and analyzed through Lyapunov arguments for asymptotic velocity consensus. Ring-road studies show that TraInX enables disturbance information to propagate ahead of the physical disturbance, reducing speed dispersion and improving recovery. Intersection studies further demonstrate improved throughput, reduced waiting and queueing, and delayed traffic saturation compared with a traffic-light baseline, highlighting the potential of multi-hop influence transfer for responsive and efficient traffic coordination

Event Details
Title: TraInX: Traffic Influence Transfer Framework for Lane-Free Traffic
Date: September 17, 2026 at 12:00 PM
Venue: ESB 244
Speaker: Mr. Chakravarthi Jada (EE24D024)
Guide: Dr. Ramkrishna Pasumarthy
Type: PHD seminar