Hengxing Cai, Jinhan Dong, Jingjun Tan, Jingcheng Deng, Sihang Li, Zhifeng Gao, Haidong Wang, Zicheng Su, Agachai Sumalee, Renxin Zhong. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

In this paper, we devise three day-to-day (DTD) road pricing schemes from their static counterparts to achieve several traffic management objectives such as regulating the traffic dynamics to the desired equilibrium, traffic restraint control, and balancing the fairness and efficiency of traffic networks, respectively. Qualitative analyses involved in the stability and convergence analysis as well as the DTD evolution of network performance admit challenges ranging from partial state stability, multiple equilibria, non-measurable system states to complex nonlinear network performance functionals. To handle these hurdles, we establish a unified stability analysis framework for qualitative analysis and control synthesis of continuous-time DTD traffic dynamics via the notion of output stability. Under this framework, by devising a proper measurable output function for the DTD dynamics in question, we can perform the relevant qualitative analyses for several continuous-time DTD traffic dynamics under different traffic control measures. For example, we investigate the DTD evolution of network performance indexes such as unfairness and inefficiency (e.g., price of anarchy (PoA)) to show how the proposed flow-dependent link toll scheme can balance the fairness and efficiency of traffic systems. We demonstrate that the proposed output stability framework can offer much more flexibility to perform qualitative analysis for these complex processes by devising suitable output functions. Finally, we show the merits of the road pricing schemes in conjunction with the output stability via numerical simulations.

This paper investigates the collaborative control of vehicular traffic for a signal-free intersection. The traffic under consideration is mixed with connected autonomous vehicles (CAVs) and human-piloted vehicles with advanced driver assistance systems (ADAS). The proposed collaborative control is of two levels. At the upper level, the objective is to minimize total delay via platoon control and dynamic priority control of conflicting movements. We formulate the platoon coordination of mixed traffic as a mixed-integer linear programming problem (MILP). This MILP determines whether adjacent vehicles will form a platoon and prioritize any two conflicting movements subject to lateral safety and rear-end safety constraints. At the lower level, we propose an eco-driving strategy to minimize the energy consumption by optimizing the speed profile of the platoon leading vehicles subject to the dynamic priority control from the upper level, i.e., the entry time to the merging zone. We deduce an analytical solution to the eco-driving problem using optimal control theory. Compared with existing benchmarks, such as the first-come-first-served policy, the proposed method outperforms the state-of-the-art controllers in reducing the total delay. Sensitivity analysis regarding the penetration rate of CAVs shows that substantial improvements can be achieved even at low to medium penetration rates of CAVs.

Efficient traffic signal control is an important means to alleviate urban traffic congestion. Reinforcement learning (RL) has shown great potentials in devising optimal signal plans that can adapt to dynamic traffic congestion. However, several challenges still need to be overcome. Firstly, a paradigm of state, action, and reward design is needed, especially for an optimality-guaranteed reward function. Secondly, the generalization of the RL algorithms is hindered by the varied topologies and physical properties of intersections. Lastly, enhancing the cooperation between intersections is needed for large network applications. To address these issues, the Option-Action RL framework for universal Multi-intersection control (OAM) is proposed. Based on the well-known cell transmission model, we first define a lane-cell-level state to better model the traffic flow propagation. Based on this physical queuing dynamics, we propose a regularized delay as the reward to facilitate temporal credit assignment while maintaining the equivalence with minimizing the average travel time. We then recapitulate the phase actions as the constrained combinations of lane options and design a universal neural network structure to realize model generalization to any intersection with any phase definition. The multiple-intersection cooperation is then rigorously discussed using the potential game theory. We test the OAM algorithm under four networks with different settings, including a city-level scenario with 2,048 intersections using synthetic and real-world datasets. The results show that the OAM can outperform the state-of-the-art controllers in reducing the average travel time.

Balancing the supply and demand for ride-sourcing companies is a challenging issue, especially with real-time requests and stochastic traffic conditions of large-scale congested road networks. To tackle this challenge, this article proposes a robust and scalable approach that integrates reinforcement learning (RL) and a centralized programming (CP) structure to promote real-time taxi operations. Both real-time order matching decisions and vehicle relocation decisions at the microscopic network scale are integrated within a Markov decision process framework. The RL component learns the decomposed state-value function, which represents the taxi drivers' experience, the off-line historical demand pattern, and the traffic network congestion. The CP component plans nonmyopic decisions for drivers collectively under the prescribed system constraints to explicitly realize cooperation. Furthermore, to circumvent sparse reward and sample imbalance problems over the microscopic road network, this article proposed a temporal-difference learning algorithm with prioritized gradient descent and adaptive exploration techniques. A simulator is built and trained with the Manhattan road network and New York City yellow taxi data to simulate the real-time vehicle dispatching environment. Both centralized and decentralized taxi dispatching policies are examined with the simulator. This case study shows that the proposed approach can further improve taxi drivers' profits while reducing customers' waiting times compared to several existing vehicle dispatching algorithms.

Serious roadside pollution in congested urban areas is an ongoing problem in many densely populated cities. While the current control measures on traffic pollution have reduced particulate matter, new solutions by emerging vehicular technologies can help protect citizens from exhaust-gas emissions. The electric vehicle (EV) is a promising solution to alleviate traffic-induced pollution in urban areas. However, traffic flow will be mixed with fuel vehicles (FVs) and EVs before the FV would be phased out. In this paper, traffic management by road pricing is introduced to reduce the tailpipe emission of the protected area by imposing environmental capacity constraints for network traffic mixed with EVs and FVs. Second-best toll pricing schemes are formulated as side constrained user equilibrium problems for both fixed demand and elastic demand cases. Both the tailpipe emission of FVs and the energy consumption of EVs are assumed to depend nonlinearly on network traffic conditions. Although the EVs do not contribute to the tailpipe emission, all vehicles contribute to congestion externality that induces more emission of FVs. Therefore, both types of vehicles are charged, but the toll on FVs is significantly higher than that on EVs. A new projected dynamics based algorithm is introduced to solve the toll from the Lagrange multiplier associated with the environmental constraint apart from the equilibrium flow. Numerical examples are conducted to evaluate the equilibrium cost and toll, and to analyze the impacts of EV penetration rate on the pricing scheme.

This paper investigates the dynamic system optimum (DSO) problem with simultaneous route and departure time assignments for a general traffic network partitioned into multiple regions. Regional traffic congestion is modeled with a well-defined macroscopic fundamental diagram (MFD) mapping the trip completion rate to the vehicular accumulation. To overcome the limitation of inconsistent flow propagation between region boundaries and the corresponding travel time, the state-dependent regional travel time function is explicitly incorporated in the flow propagation of the conventional MFD dynamics. From a systems perspective, the traffic dynamics within a region can be regarded as a dynamic system with an endogenous time-varying delay depending on the system state. Equilibrium condition for the DSO problem is analytically derived through the lens of Pontryagin minimum principle and is compared against the static SO counterpart. The structure of path specific marginal cost is analyzed regarding the path travel cost and early-late penalty function. In contrast to existing analytical methods, the proposed method is applicable for general MFD systems without linearization of the MFD dynamics. Neither approximation of the equilibrium solution nor constant regional delay assumption is required. Numerical examples are conducted to illustrate the characteristics of DSO traffic equilibrium and the corresponding marginal cost together with other dynamic external costs.