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• Scenario where vehicles adhere to or deviate from the system optimal route guidance. • Reinforcement learning approach to dynamic system optimal assignment problem. • Multi-agent reinforcement learning to assess the impact of non-compliance. • Exploration of the balance between system efficiency and individual user preferences. We consider a scenario where the transportation management center (TMC) guides future autonomous vehicles (AVs) toward optimal routes, aiming to bring the network in line with the system optimal (SO) principle. However, achieving this requires a joint decision-making process, while users may be non-compliant with the TMC's route guidance for personal gain. This paper models a future transportation network with a microscopic simulation, to introduce a novel concept of mixed equilibrium. In this framework, AVs follow the TMC's SO route guidance, while users can dynamically choose to either comply or manually override this autonomy based on their own judgment. We initially model a fully compliant scenario, where the centralized Q-network, analogous to a TMC, is trained using reinforcement learning (RL) to minimize total system travel time (TSTT), providing optimal routes to users. Subsequently, we extend the problem setting to a multi-agent reinforcement learning (MARL) scenario, where users can comply or deviate from the TMC's guidance based on their own decision-making. Through neural fictitious self-play (NFSP), we employ a modulating hyperparameter to investigate the impact of varying degrees of non-compliance on the overall system. Results indicate that our RL approach holds significant potential for addressing the dynamic system optimal assignment problem. Remarkably, the TMC's route guidance retains the essence of SO while integrating some level of non-compliance. However, we also demonstrate that dominant user-centric decision-making may lead to system inefficiencies while creating disparities among users. Our framework serves as an innovative tool in an AV-dominant future, offering a realistic perspective on network performance that aids in formulating effective traffic management strategies. [ABSTRACT FROM AUTHOR]

The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed. However, deploying these approaches for large-scale networks poses significant challenges. In this paper, we leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate model for traffic assignment, specifically user equilibrium traffic assignment problems. Our model integrates an adaptive graph attention mechanism with auxiliary "virtual" links connecting origin–destination node pairs, This integration enables the model to capture spatial traffic patterns across different links, By incorporating the node-based flow conservation law into the overall loss function, the model ensures the prediction results in compliance with flow conservation principles, resulting in highly accurate predictions for both link flow and flow-capacity ratios. We present numerical experiments on urban transportation networks and show that the proposed heterogeneous graph neural network model outperforms other conventional neural network models in terms of convergence rate and prediction accuracy. Notably, by introducing two different training strategies, the proposed heterogeneous graph neural network model can also be generalized to different network topologies. This approach offers a promising solution for complex traffic flow analysis and prediction, enhancing our understanding and management of a wide range of transportation systems. • A novel heterogeneous GNN-based approach is proposed for static traffic assignment. • GNN model includes "real" roadway links and "virtual" links connecting OD pairs. • Flow conservation loss is added as a physics-based term in the loss function. • Model is generalizable to different network topologies and OD demands. [ABSTRACT FROM AUTHOR]

The design of dynamic tolling algorithms for managed lanes is complex and challenging, in part due to the often conflicting and differing objectives of the operator, travelers and the regulator. In this paper, we propose a framework for personalized pricing of managed lanes that combines elements of prediction, optimization, and personalization to dynamically determine displayed tolls and personalized discounts to travelers. The proposed approach involves an online bi-level optimization formulation consisting of coupled system-level and user-level optimization problems. The system optimization problem uses real-time traffic predictions (from a simulation-based dynamic traffic assignment model) to periodically determine displayed tolls and a discount control policy that maximize a multi-component system-level objective (considering different stakeholders with flexible policy hyperparameters). The user optimization problem consistently determines a personalized discount to offer to each individual traveler based on individual-specific preferences subject to the displayed toll and discount policy in effect. At the heart of both the system and user optimization problems is an individual-specific behavioral model (of choice between the managed lane and general purpose lanes) that incorporates state dependence (habit and loyalty formation), unobserved inter- and intra-consumer heterogeneity, and corrections for price endogeneity. Rigorous closed-loop simulations of an operational managed lane facility using real data demonstrate that the proposed personalized dynamic tolling approach improves operator revenue and managed lane usage, traveler benefits and equity, and reduces congestion relative to the existing tolling policy. • Propose a framework for personalized tolling of managed lanes. • Displayed tolls and personalized discounts are determined in real-time. • A multi-component objective function incorporates the interests of the operator, the travelers and the regulator. • Behavioral model incorporates state dependence, unobserved inter- and intra-consumer heterogeneity, and corrects for price endogeneity. • Improves operator revenue and managed lane usage, traveler benefits and equity, and reduces congestion relative to status quo. [ABSTRACT FROM AUTHOR]

Airport runway assignment and gate assignment are both crucial problems for large multirunway airports. A cooperative schedule for the runway and gate can have a significant effect on airport ground operation efficiency and safety. This paper focuses on the joint assignment problem of runways and gates considering parking time differences and airline preferences. A quadratic model is established to reduce taxiing fuel consumption loss and increase the robustness of gate assignment. An improved branch-and-price algorithm is proposed by utilizing an improved pulse algorithm and various acceleration strategies to improve the solution performance. The actual flight data at an international hub airport are utilized to validate the proposed model. The results indicate that the joint assignment model can effectively reduce ground taxiing loss, optimize gate assignment robustness, and increase the utilization rate of contact gates. The proposed algorithm also achieves better computational accuracy and efficiency. • Proposed joint runway–gate assignment model can reduce the taxiing loss and gate robustness loss. • The model optimizes the taxiing and gate robustness while ensuring airline's preference. • A better solution is obtained by an improved branch-and-price method. • Experiments are designed to verify the value of the joint model. [ABSTRACT FROM AUTHOR]

This paper studied the range-constrained traffic assignment problem (RTAP), where heterogeneous range anxiety is considered among the driving population by electric vehicles (EVs). In order not to get stranded en-route, each EV driver is assumed to have his/her own driving range limit for being able to complete the trip. As a result, two types of multi-class RTAP can be defined through discrete or continuous distributed range anxiety. Given path-based side constraint structures, we proposed two variational inequality (VI) formulations for modeling discrete and continuous RTAPs, where the former is finite-dimensional according to a discrete number of user classes and the latter is infinite-dimensional accounting for an infinite number of user classes. We reformulate the continuous RTAP into finite-dimensional by merging adjacent EV drivers into one group. A unified path-based solution framework is developed to solve the two RTAPs, built upon the gradient projection algorithm. We design column generation and dropping schemes to adaptively maintain compact path sets and an inner equilibration strategy to accelerate convergence. Finally, a small network is used to examine the correctness and effectiveness of proposed models, and a large Winnipeg network is adopted to evaluate the impacts of stochastic driving range on network flows and computation costs. Numerical results provide compelling evidence of the outstanding superiority of the proposed algorithm, and show that EV drivers with heightened sensitivity towards range anxiety may contribute to the emergence of critical links experiencing severe congestion. • General VI formulations for range-constrained traffic assignment problem (RTAP). • Unified path-based solution framework is developed to solve two types of RTAPs. • We design column management and inner equilibration strategies. • Model features and solution efficiency of RTAPs are illustrated using two networks. [ABSTRACT FROM AUTHOR]