Jieming Chen

Cooperative control of connected automated vehicles (CAVs) offers a promising solution for reducing traffic congestion and accidents. However, existing optimization-based and search-based methods for trajectory planning and vehicle scheduling struggle with real-time multi-vehicle control. This paper introduces a hybrid bi-level approach that nests optimization modelling within deep reinforcement learning (DRL) to jointly optimize vehicle sequences, lane selections, and trajectories, providing a rapid, safe, and high-quality solution to enhance traffic performance at multi-lane freeway merging sections. Specifically, we approach the problem of lane selection and vehicle sequencing for multiple vehicles as a multi-step decision-making process. At the upper level, we design a DRL agent with an attention-based encoder-decoder structure that auto-regressively constructs driving sequences and lane choices. It generates a probability matrix to select the next passing vehicle and target lane based on prior decisions at each step. The attention mechanism enables the centralized upper level to adapt to scenarios with varying vehicle counts without the need to retrain. At the lower level, we formulate a model predictive control (MPC) planner to generate safe trajectories. The resulting travel delay guides the upper-level DRL agent learning to maximize overall traffic efficiency. Moreover, we introduce a leader-and-lane-specific credit assignment mechanism that leverages domain knowledge to link each action with associated travel delays. This mechanism enables the agent to accurately recognize the impact of decisions on total delay, enhancing learning performance. Simulation results suggest that the proposed approach’s superior real-time performance and scalability from several to over a dozen vehicles, making it well-suited for practical automated merging tasks.

The problem of considering mixed traffic in lane-free scenarios is intriguing. The concept of strips plays a crucial role in understanding controller design, making it worthwhile to explain for better clarity. Additionally, how the influence of multi...

Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network’s resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures.

In response to the European Union (EU)’s sanctions on Russian oil products, tanker shipping firms may adopt two strategies to reoptimize their shipping networks. The first strategy is to switch the flag states of tankers that are not eligible to operate on certain routes. The second strategy is to reposition tankers based on their flag states, i.e., moving those tankers that are eligible from other groups to specified routes. To help tanker shipping firms minimize the total operating cost during the planning horizon in the context of EU oil sanctions, including costs of fleet repositioning, flag switching, and fuel, this study investigates an integrated problem of fleet repositioning, flag switching, transportation scheduling, and speed optimization considering the dynamic relationships among fuel consumption, speed, and load. By formulating the problem as a nonlinear integer programming model and applying various linearization techniques to convert the nonlinear model into a linear optimization model solvable by off-the-shelf linear optimization solvers, this study demonstrates the practical application potential of the proposed model, with the longest solution time of less than two hours for a numerical instance with seven routes. Furthermore, through sensitivity analyses on important factors including unit fuel prices, crude oil transportation demand, and the tanker repositioning cost, this study provides managerial insights into the operations management of tanker shipping firms.

We propose a simple and novel method to actively resolve the temporary congestion caused by a freeway mainline vehicle’s facilitating maneuver of creating a gap for on-ramp merging vehicles, under under-critical mainline conditions. We first present an analytical finding derived from the kinematic wave model with a triangular fundamental diagram. That is, when the prevailing mainline traffic is under-critical, the total delay of the mainline vehicles affected by the gap creation does not depend on the choice of speed by which the gap is created, namely the facilitating speed. This is because the recovery wave speed is constantly equal to the characteristic wave speed of the congestion regime, $-w$ . In light of this observation, to improve mainline traffic efficiency, we propose the strategy of active congestion resolving, which features a recovery wave of a speed higher than $-w$ . Characterizing CAVs’ car-following behaviors by Newell’s simplified car-following model, we analytically show that such a recovery wave can be achieved by properly modifying the values of the affected CAVs’ car-following characteristic parameters and adopting the modified values in a proper way during congestion resolving. Then, in the presence of this active congestion resolving strategy, an optimization program is formulated to seek an optimal facilitating speed that can balance between traffic efficiency and speed variation. Simulation experiments are conducted to validate the effectiveness of the proposed method.

Intensive interactions among vehicles at freeway on-ramp merging areas lead to congestion and accidents. The emergence of connected automated vehicles (CAVs) has shown great potential to improve this issue. In this paper, a mixed integer nonlinear programming (MINLP) model is proposed and solved for the task of a cooperative merging of two traffic streams at a freeway on-ramp merging section. The proposed model simultaneously optimizes multiple vehicles’ trajectories and their merging sequence to improve traffic efficiency and ensure safety. Unlike conventional treatments, which match one mainline facilitating vehicle with one merging vehicle, the proposed model determines the optimal number of facilitating vehicles and which mainline vehicles should serve as the facilitating vehicles to cooperatively minimize disruption from ramps. The safety and feasibility of the planned vehicle trajectories are guaranteed at any time. We propose an integrated solution algorithm that incorporates an iterative linear programming method into a novel search process based on a necessary condition for optimality that we identify and prove. The algorithm is highly efficient because it enjoys a significantly reduced search space. The proposed approach, consisting of the MINLP model and the solution algorithm, is evaluated under different traffic demands and mainline-ramp demand ratios and real vehicle arrival patterns from the NGSIM dataset. The performance of the proposed method outperforms benchmark CAV control algorithms, and the computational efficiency is promising for real-time automated merging tasks.

An amperometric biosensor for the determination of ethanol has been constructed. It comprises a multiwalled carbon nanotubes (MWNTs) conduit, a Nafion binder, and an alcohol dehydrogenase (ADH) function. The measurement of ethanol is based on the signal produced by β-nicotinamide adenine dinucleotide (NADH), the product of the enzymatic reaction. The MWNTs are cylindrical with an outer diameter in the range 40–60 nm, an inner diameter in the range 2–5 nm, and a length of up to several micrometers. The homogeneity of the resulting nanobiocomposite film was characterized by field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM). The performance of the MWNTs-Nafion-ADH nanobiocomposite modified glassy carbon electrode was examined using cyclic voltammetry and amperometry in presence of NADH and in the presence of ethanol. The electrocatalytic activity of MWNTs towards the oxidation of NADH has allowed an effective low-potential amperometric determination of ethanol. In the case of 6 mgmL⁻¹ ADH, the MWNTs-Nafion-ADH nanobiocomposite film displayed a sensitivity of 830 nAmM⁻¹, a linear range up to 0.1 mM, a detection limit of 3 μM, and a response time of about 4 s.