Method of Assortment Control for Sector Boundary Traffic Signals Using Organic Computing

The research focuses on developing anassortment control procedure for traffic signals at sector boundaries using organic computing principles. This study lies at the intersection of urban traffic signal control and artificial intelligence. The proposed procedure comprises various modules, including traffic flow monitoring, self-optimization, self-modification, evolutionary learning, self-assessment, and self-adaptation. The objective is to achieve efficient assortment between traffic signals at sector boundaries, thus preventing congestion and traffic blockages in the intersecting areas

Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions

The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.View the NCST Project Webpag

DEVELOPMENT AND VALIDATION OF DYNAMIC PROGRAMMING ALGORITHM FOR ECO APPROACH AND DEPARTURE

Eco Approach and Departure (Eco-AnD) is a Connected and Automated Vehicle (CAV) technology developed to reduce energy for crossing a signalized intersection or set of intersections in a corridor that features vehicle to infrastructure (V2I) communication capability. Eco-AnD technology uses the information of the signal phase and timings (SPaT) received from the V2I communication to optimize the vehicle’s speed profile and produce an energy-efficient maneuver to cross the intersection. The Eco-AnD algorithm is devised for two vehicles (GM-Volt Gen II & GM-Bolt), both with different powertrain architectures but capable of working in electric-only mode. In simulations, the developed algorithm showed an energy-saving potential of 70-90 kJ per intersection around the corridor of the MTU drive cycle for both vehicles. For the RSU loop (a subset of MTU drive cycle) up to 8 % of energy reduction is observed. Vehicle level testing of the optimized speed profiles was carried out at the American Center of Mobility (ACM) on GM-Volt Gen II to demonstrate an energy-saving of 40-50 kJ per intersection on real road conditions