TrafficMCTS: A Closed-Loop Traffic Flow Generation Framework with Group-Based Monte Carlo Tree Search

Digital twins for intelligent transportation systems are currently attracting great interests, in which generating realistic, diverse, and human-like traffic flow in simulations is a formidable challenge. Current approaches often hinge on predefined driver models, objective optimization, or reliance on pre-recorded driving datasets, imposing limitations on their scalability, versatility, and adaptability. In this paper, we introduce TrafficMCTS, an innovative framework that harnesses the synergy of groupbased Monte Carlo tree search (MCTS) and Social Value Orientation (SVO) to engender a multifaceted traffic flow replete with varying driving styles and cooperative tendencies. Anchored by a closed-loop architecture, our framework enables vehicles to dynamically adapt to their environment in real time, and ensure feasible collision-free trajectories. Through comprehensive comparisons with state-of-the-art methods, we illuminate the advantages of our approach in terms of computational efficiency, planning success rate, intent completion time, and diversity metrics. Besides, we simulate highway and roundabout scenarios to illustrate the effectiveness of the proposed framework and highlight its ability to induce diverse social behaviors within the traffic flow. Finally, we validate the scalability of TrafficMCTS by showcasing its prowess in simultaneously mass vehicles within a sprawling road network, cultivating a landscape of traffic flow that mirrors the intricacies of human behavior

Phase Fluctuation Analysis in Functional Brain Networks of Scaling EEG for Driver Fatigue Detection

The characterization of complex patterns arising from electroencephalogram (EEG) is an important problem with significant applications in identifying different mental states. Based on the operational EEG of drivers, a method is proposed to characterize and distinguish different EEG patterns. The EEG measurements from seven professional taxi drivers were collected under different states. The phase characterization method was used to calculate the instantaneous phase from the EEG measurements. Then, the optimization of drivers’ EEG was realized through performing common spatial pattern analysis. The structures and scaling components of the brain networks from optimized EEG measurements are sensitive to the EEG patterns. The effectiveness of the method is demonstrated, and its applicability is articulated.</p

Bis(μ-naphthalene-1,8-dicarboxyl­ato-κ2 O 1:O 8)bis­[aqua­bis­(N,N′-dimethyl­formamide-κO)copper(II)]

In the centrosymmetric dinuclear title complex, [Cu2(C12H6O4)2(C3H7NO)4(H2O)2], the coordination environment of each Cu(II) atom displays a distorted CuO5 square-pyramidal geometry, which is formed by two carboxyl­ate O atoms of two μ-1,8-nap ligands (1,8-nap is naphthalene-1,8-dicarboxyl­ate), two O atoms of two DMF (DMF is N,N′-dimethyl­formamide) and one coordinated water mol­ecule. The Cu—O distances involving the four O atoms in the square plane are in the range 1.9501 (11)–1.9677 (11) Å, with the Cu atom lying nearly in the plane [deviation = 0.0726 (2) Å]. The axial O atom occupies the peak position with a Cu—O distance of 2.885 (12) Å, which is significantly longer than the rest of the Cu—O distances. Each 1,8-nap ligand acts as bridge, linking two CuII atoms into a dinuclear structure. Inter­molecular O—H⋯O and C—H⋯O hydrogen-bonding inter­actions consolidate the structure