FST Graduate Research Topic

Research Topic: Adaptive Traffic Light Control in Smart Cities
Suggested By: Harris Wang
Last Update: September 28th, 2025

Constrained Object Hierarchies (COH) provides a neuroscience-inspired theoretical framework for modeling Artificial General Intelligence (AGI). As a flexible and robust foundation, COH supports the representation of general intelligent systems across diverse domains. Formally, a COH is defined as a 9-tuple: O = (C, A, M, N, E, I, T, G, D) Where: C: Components – sub-objects organized hierarchically A: Attributes – state variables describing object properties M: Methods – executable actions associated with components N: Neural Components – adaptive models for learning and inference E: Embedding Neural Component – semantic representation of the COH I: Identity Constraints – rules ensuring object integrity T: Trigger Constraints – event-condition-action rules for dynamic behavior G: Goal Constraints – optimization objectives guiding intelligent behavior D: Constraint Daemons – real-time monitors enforcing system constraints Based on the COH framework, the modelling process and result of Adaptive Traffic Light Control in Smart Cities are as follows: System Description and Significance: Intelligent traffic management systems optimize vehicle flow through coordinated signal timing, reducing congestion and improving urban mobility. These systems are significant for addressing growing urbanization challenges and reducing the economic and environmental costs of traffic congestion. Analysis and Design: The system must coordinate multiple intersections while responding to real-time traffic conditions and emergency situations. Neural components predict traffic flow patterns, while identity constraints ensure safety by preventing conflicting green signals. The hierarchical structure enables both local optimization and city-wide coordination. Modelling Solution: C: {Intersection1, Intersection2, ... TrafficManagementCenter} A: vehicle_count_per_lane, queue_length, current_light_phase, emergency_vehicle_proximity M: change_light_phase(), extend_green_time(), create_green_wave() N: TrafficFlowPredictor, CongestionPropagationModel E: Embedding of city-wide traffic flow efficiency I: Conflicting light phases must never be green simultaneously (safety) T: IF emergency_vehicle_approaches THEN preempt_light_sequence G: Minimize_average_wait_time AND Minimize_total_congestion D: GridlockPreventionDaemon monitoring queue lengths to prevent system-wide lock-up The COH model demonstrates how urban infrastructure can be managed through distributed intelligence with central coordination, using predictive capabilities to anticipate congestion while maintaining absolute safety guarantees. You may choose this topic and pursue one of the following routes: 📝 Essay Survey existing methodologies for modeling, designing, and implementing similar intelligent systems. Include: Literature synthesis COH framework analysis Critical evaluation of competing approaches 🛠️ Project Design and implement the system using the COH model. Include: System architecture and design Development and testing Demonstration of core functionality 🎓 Thesis Explore COH’s theoretical foundations in depth. Include: Proposed enhancements or extensions Implementation and evaluation Development of an advanced intelligent system grounded in COH principles