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To investigate the heterogeneity of car-following behaviors across different vehicle combinations from the perspective of driver visual characteristics, the NGSIM dataset from I-80 and US-101 highways was selected and distinct car-following segments were extracted for analysis. Firstly, all the effective vehicle trajectories were extracted and categorized into different vehicle types based on their widths, resulting in four combination types of car-following segments. Visual angle and its change rate were introduced as variables representing driver visual characteristics. Additionally, one-way analysis of variance (ANOVA) was used to compare these variables with traditional ones. The driver's visual characteristic variables were then incorporated to improve the full velocity difference (FVD) model. Genetic algorithms were employed to calibrate the model under different car-following types, revealing pronounced behavioral variations. After implementing the enhanced drivers' visual angle (DVA) model, substantial reductions in calibration and validation errors were observed, with calibration errors decreasing by 51.93% and 42.22% and validation errors decreasing by 56.61% and 45.26%. This indicates the DVA model's remarkable adaptability and stability. Lastly, through a sensitivity analysis of errors, the DVA model demonstrated greater robustness toward the improved error evaluation function. By integrating drivers' visual characteristics, this study provides in-depth insights into heterogeneous car-following behaviors, enhancing our understanding of driver behaviors and micro-traffic simulation systems., Author(s): Congcong Bai [1,2]; Jun Jing [3]; Bokun Liu [4]; Wenbin Yao [1]; Chengcheng Yang [1]; Adjé Jérémie Alagbé [1]; Sheng Jin (corresponding author) [1,2,5] 1. Introduction Car-following models have [...]

Highlights With the advent of the 5G era, electromagnetic pollution has been increasingly serious. Structural electromagnetic wave (EW) absorbing composites play a key role in both civil and military applications. However, the laminated plate structure and laminated honeycomb structure EW‐absorbing composites were prone to delamination and low EW absorbing performance. To enhance the EW absorption and mechanical properties of composite materials, this study successfully developed a novel three‐dimensional hexagonal honeycomb woven composites (3D‐HHWC). 3D‐HHWC with a three‐dimensional hexagonal honeycomb woven fabric (3D‐HHWF) as the reinforced materials was manufactured by the vacuum‐assisted resin transfer molding (VARTM) process with epoxy resin, carbon black (CB), multi‐walled carbon nanotubes (MWCNTs), and carbonyl iron powder (CIP) as functional materials. The EW absorption performance and bending mechanical properties of 3D‐HHWC with different structural parameters were compared and analyzed. The test results demonstrated that the 3D‐HHWC (H2‐3) achieved a remarkable minimum reflection loss of −38.1 dB with the effective electromagnetic absorption band (EAB) 13.8 GHz. Additionally, CST provided a clear explanation of the electromagnetic field distribution elucidating the mechanism for EW absorption. While the 3D‐HHWC (H2‐3) had a maximum bending load 6603.9 N without obvious delamination phenomenon. Furthermore, finite element simulation clarified internal stress distribution explaining the failure mechanisms. In conclusion, the 3D‐HHWC exhibited outstanding EW absorption and mechanical properties for widespread application in military and aerospace fields. The 3D‐HHWC was formed by VARTM process with epoxy resin, CB, MWCNTs, and CIP. The 3D‐HHWC had excellent EM performance with RLmin −38.1 dB and EAB 13.8 GHz. CST provided a clear explanation of the electromagnetic field distribution. The 3D‐HHWC had a maximum bending load 6603.9 N without obvious delamination. FEM clarified internal stress distribution to explain the failure mechanisms. [ABSTRACT FROM AUTHOR]

In order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi‐line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous public transport vehicles (APTVs) dedicated lanes is developed, which enables APTVs to pass through intersections without stopping under conditions that almost have no effect on regular vehicles (RVs). Based on this, a speed guidance strategy of APTVs under connected environment is proposed. After guiding APTVs into the overlapping route segments at a reasonable interval, the optimization goal of maintaining the independent running headway of each bus line to the maximum extent is realized. The simulation verification based on three actual overlapping lines in Hangzhou shows that only the combination of signal coordination considering the characteristics of APTVs and speed guidance can realize the full benefits of bus operation based on dedicated APTVs lane. [ABSTRACT FROM AUTHOR]

Commuting pattern is one of the most important travel patterns on the road network; the analysis of commuting pattern can provide support for public transport system optimization, policy formulation, and urban planning. In this study, a framework of the key commuting route mining algorithm based on license plate recognition (LPR) data is proposed. And the proposed algorithm framework can be migrated to any similar spatiotemporal data, such as GPS trajectory data. Commuting pattern vehicles are first extracted, and then, the spatiotemporal trip chains of all commuting pattern vehicles are mined. Based on the spatiotemporal trip chains, the spatiotemporal similarity matrix is constructed by dynamic time warping (DTW) algorithm. Finally, the characteristics of commuting pattern are analysed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Different from other researches that analyse the commuting pattern using machine learning algorithms based on all data, this study first extracts commuting pattern vehicles and then designs a key commuting route mining algorithm framework for commuting pattern vehicles. Taking Hangzhou as an example, through the framework of mining algorithm proposed in this study, the commuting pattern characteristics and key commuting routes in Hangzhou have been successfully excavated, and policy suggestions based on the analysis results have also been put forward., Author(s): Wenbin Yao [1]; Caijun Chen [2]; Hongyang Su [1]; Nuo Chen [1]; Sheng Jin (corresponding author) [1,3]; Congcong Bai [1] 1. Introduction With the increasing number of motor vehicles, [...]