This study investigates the potential of link- and path-based incentives to mitigate congestion in urban transportation networks under a budget constraint. Both incentive schemes are formulated as non-linear optimisation problems with complementarity constraints. Mathematically, it is demonstrated that the feasible region of the link-based model is a subset of the feasible region of the path-based model under the same budget constraint. Consequently, path-based incentives exhibit greater potential to shift the user equilibrium flow pattern toward the system optimum compared to link incentives. A column generation-based iterative solution technique, which generates new paths at each iteration, is devised to efficiently solve both optimisation problems. Numerical experiments conducted for various transport networks also highlight the efficiency and scalability of the proposed algorithm, and the superiority of path-based incentives in reducing total travel time in urban transportation networks.

Max-pressure (MP) control has emerged as a prominent real-time network traffic signal control strategy due to its simplicity, decentralized structure, and theoretical guarantees of network queue stability. Meanwhile, advances in connected vehicle (CV) technology have sparked extensive research into CV-based traffic signal control. Despite these developments, few studies have investigated MP control in heterogeneously distributed and partially CV environments while ensuring network queue stability. To address these research gaps, we propose a CV-based MP control (CV-MP) method that leverages real-time CV travel time information to compute the pressure, thereby incorporating both the spatial distribution and temporal delays of vehicles, unlike existing approaches that utilized only spatial distribution or temporal delays. In particular, we establish sufficient conditions for road network queue stability that are compatible with most existing MP control methods. Moreover, we pioneered the proof of network queue stability even if the vehicles are only partially connected and heterogeneously distributed, and gave a necessary condition of CV observation for maintaining the stability. Evaluation results on an Amsterdam corridor show that CV-MP significantly reduces vehicle delays compared to both actuated control and conventional MP control across various CV penetration rates. Moreover, in scenarios with dynamic traffic demand, CV-MP achieves lower spillover peaks even with low and heterogeneous CV penetration rates, further highlighting its effectiveness and robustness.

Smart traffic systems, like those using wellestablished methods such as SCOOT, SCATS and TUC, aim to improve traffic flow by dynamically adjusting signal timings based on real-time traffic conditions. Traffic engineers need to understand the objective functions behind traffic signal control to analyze, improve, and optimize network performances. However, different jurisdictions, different operators and competing interests imply that the underlying objective functions governing traffic signal control might not be publicly known with sufficient detail (e.g. to preserve Intellectual Property Rights). A method for discovering these functions is therefore needed, particularly to enable better cooperation among stakeholders. In this work, we train computer models to mimic the decisions made by smart traffic light systems. Using data from a simulated traffic network (with virtual sensors tracking vehicles), we test a variety of supervised models, ranging from simple decision trees to more complex neural networks. Our results show these models can accurately mimic the underlying system's actions, achieving up to 99% accuracy. This work demonstrates that supervised learning can serve as a powerful tool for uncovering hidden traffic control functions by training models to replicate the system's decisions. By analyzing these models, we can then infer the key factors influencing signal control, thereby gaining insights into the underlying objective function.

Max-pressure (MP) control has proven effective at stabilizing network queues and improving traffic throughput in large-scale urban road networks. However, conventional MP controllers based on connected vehicle (CV) data face two critical limitations: network stability diminishes when connected vehicle (CV) penetration rates are low, and significant privacy concerns arise when utilizing individual vehicle data. To address these challenges, this paper proposes a novel Private-MP controller that fuses data from both fixed-location detectors and CVs in an architecture of mobile edge computing. To fully safeguard CV privacy, including macro-route information and micro-trajectory information, Private-MP employs a privacy-preserving mechanism that combines homomorphic encryption with an adaptive randomized response strategy. Simulation studies on a network with five intersections showed that despite some increases in average vehicle delay due to privacy protection, Private-MP still ensures a more robust performance on average vehicle delay than CV-based MP in low penetration rate scenarios and outperforms traditional detector-based MP control while improving fairness among connected and non-connected vehicles.

This study addresses the scalability challenges of the Mixed-Fleet Multi-Terminal Electric Bus Scheduling Problem by exploring various heuristic and metaheuristic approaches applied to large urban networks. A novel Repeated Local Search (RLS) algorithm is developed to optimize full-day scheduling, incorporating key factors such as fleet assignment, charging constraints, and deadheading costs, while accounting for limited charging infrastructure. The RLS method generates initial greedy yet feasible schedules for a mixed fleet of electric and hybrid buses, serving as the foundation for two metaheuristic strategies: Simulated Annealing and a Genetic Algorithm. The Simulated Annealing approach is implemented in two variants: one integrating a Mixed-Integer Linear Programming (MILP)-based move, and the other using an RLS-based move to reschedule trip chains while maintaining feasibility. Meanwhile, the Genetic Algorithm employs repair mechanisms to correct infeasible solutions arising during the crossover process. To evaluate these methodologies, a three-phase experimental framework is employed: (1) stress-testing a MILP model under various fleet and infrastructure conditions, (2) benchmarking MILP performance against metaheuristic methods on small-scale instances, and (3) conducting a comparative analysis of metaheuristics across small, medium, and real-size urban scenarios. The urban-scale instances are derived from real-world public transit timetables in Luxembourg City, encompassing 1,084 trips, 12 terminals, 10 bus lines, and full-day operations. Results indicate that the proposed metaheuristic approaches achieve solutions comparable to exact MILP formulations in small-scale cases while offering substantial scalability improvements for larger networks. Each algorithm exhibits distinct advantages and trade-offs, highlighting the importance of selecting an appropriate method based on the specific scenario and computational constraints. These findings extend prior research on smaller instances and suggest that as urban transit systems transition to electric fleets, the marginal operational benefits for transit agencies may diminish with increasing network size.