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In this paper, we examine the impact of vehicles executing a full turn or U-turn in a single-lane roundabout system using a cellular automaton model. We investigate how increasing the number of these U-turning vehicles affects traffic flow characteristics and energy dissipation. Our findings reveal that as the prevalence of U-turning vehicles rises, the phase diagram undergoes significant changes; the maximum current phase expands, detrimentally impacting the free flow and congestion phases, while giving rise to a jamming phase. This shift results in a gradual increase in capacity at circulating lanes and a steady decline at entry/exit lanes. We also explore the role of aimless vehicles — those circulating without a fixed destination. As the proportion of such vehicles augments, it fosters an enlargement of the maximum current phase at the expense of the free flow and congestion phases. Furthermore, these vehicles influence energy dissipation across the three lanes of a roundabout. Significantly, we elucidate that variations in the percentage of these specific vehicular groups critically affect CO2 emissions. Our research unravels vital insights into optimizing roundabout management to enhance traffic flow and reduce environmental impact. [ABSTRACT FROM AUTHOR]

Traffic metering parameters in the CBD of Akure such as traffic composition and volume-capacity ratio were collected and evaluated to determine the levels of service of the selected major roads namely Oba Adesida road (directions A and B): direction A from Cathedral junction to 'A' Division and Direction B from 'A' Division to Cathedral junction and Arakale road (directions C and D): direction C from Isikan roundabout to NEPA roundabout and direction D from NEPA roundabout to Isikan Roundabout. The traffic composition analysis revealed the passenger car/taxi as the predominant mode while poor parking system, trading activities along the carriageway, bad roads, construction activities were identified as factors affecting the levels of service. [ABSTRACT FROM AUTHOR]

This paper focuses on the cooperative driving challenges of connected and automated vehicles (CAVs) at single-lane roundabouts. First, a geometric path planning method is proposed for CAVs navigating a single-lane roundabout. Based on this method, a vehicle roundabout model is established. Four potential traffic scenarios for CAVs are established, and the optimal arrival times at conflict points are analyzed. By correlating the optimal arrival times at conflict points with the optimal entry times into the roundabout, the multi-vehicle coordination problem in complex intersections is simplified to a speed control issue during entry. Utilizing the principles of optimal control and Pontryagin minimization, two speed optimization strategies are proposed. Finally, MATLAB is employed for simulation analysis. The results indicate that the control strategy proposed in this paper enables the system to clearly identify potential conflicts between vehicles and implement an optimal control strategy, ensuring that vehicles can navigate the roundabout efficiently in terms of time and fuel without collisions. Additionally, the minimum time interval is established at 0.2 seconds to completely prevent vehicle collisions. In this study, the fusion problem involving two vehicles at a single conflict point is further expanded to encompass multiple vehicles at multiple conflict points. Thus, the efficient scheduling of multiple vehicles in single-lane roundabouts is realized. [ABSTRACT FROM AUTHOR]

This document provides a summary of a book that explores the challenges and advancements in the guidance, navigation, and control (GNC) of autonomous and transport vehicles. It emphasizes the need for vehicles to operate autonomously and efficiently in congested city environments and discusses the development of collaborative GNC for capabilities like platooning. The document also mentions the impact of policies on GNC, such as the authorization of eVTOL and drone operations by the FAA in the United States. It highlights the growing research areas in vehicle navigation and control, including sensor fusion, trajectory optimization, and control challenges. The document concludes by encouraging readers to explore the publications in the Special Issue to gain a deeper understanding of the emerging research priorities in GNC for a diverse range of vehicles. [Extracted from the article]

AbstractWith increasing driving automation, driving tasks are shifting from drivers to automation systems and their roles are gradually changing from drivers to passengers. While efforts have been made to explore influencing factors of highly automated vehicles (HAVs) usage intention, few studies have linked role adaptation (RA) to variables from existing acceptance models. To fulfill this research gap, a HAV acceptance model was established based on RA, and other factors (i.e., situational trust, anxiety, and perceived usefulness [PU]). The proposed model validity was verified by subjective ratings collected from a driving simulator experiment involving 105 participants each of whom rode vehicles with three different automated driving styles. Results revealed that RA was an important factor in forming HAVs acceptance during initial human-automation interaction stages and could be increased by improving situational trust, PU, or reducing anxiety. Practically, these results can provide valuable guidance for enhancing consumers’ HAV acceptances. [ABSTRACT FROM AUTHOR]

The paper aims to investigate the effect of the free-flow right-turn bypass lane at single-lane roundabouts in terms of overall delay improvements. In total, 47 different cases of various entry flows per approach were examined, utilizing 189 different distribution rates in left-turn, through and right-turn movements per case, resulting thus in 8883 discrete scenarios. The multiple linear regression analysis that followed revealed the most critical parameters that affect the overall roundabout's operational performance in terms of delay when a free-flow right-turn bypass lane is introduced. The model extracted herein, which is statistically adequate to serve as a useful tool in roundabout design, at least during the planning stage, can identify whether the installation of a free-flow right-turn bypass lane can improve the total roundabout's delay. Further analysis is required to investigate in more detail potential effects of additional parameters, such as multilane roundabouts, the existence of more than one bypass lanes and, more importantly, the presence of pedestrians, which may levy further constraints. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have gained extensive attention in recent years, both in academia and industry. For these self-driving vehicles, decision-making in urban environments poses significant challenges due to the unpredictable behavior of traffic participants and intricate road layouts. While existing decision-making approaches based on Deep Reinforcement Learning (DRL) show potential for tackling urban driving situations, they suffer from slow convergence, especially in complex scenarios with high mobility. In this paper, we present a new approach based on the Soft Actor-Critic (SAC) algorithm to control the autonomous vehicle to enter roundabouts smoothly and safely and ensure it reaches its destination without delay. For this, we introduce a destination vector concatenated with extracted features using Convolutional Neural Networks (CNN). To evaluate the performance of our model, we conducted extensive experiments in the CARLA simulator and compared it with the Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) models. Qualitative results reveal that our model converges rapidly and achieves a high success rate in scenarios with high traffic compared to the DQN and PPO models. [ABSTRACT FROM AUTHOR]

Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient (PE-DDPG) for AutonomousDriving in the Roundabouts. Specifically, themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework, enhancing the vehicle's capability to comprehend complex roundabout environments and make decisions. Furthermore, the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios, effectively mitigating traffic bottlenecks and augmenting throughput efficiency. Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO, and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process, the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles (AVs). Generally, the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data. [ABSTRACT FROM AUTHOR]

This article focuses on the impact of rural road provision on the macroeconomic objective of full employment. It deals with the extent to which roads act as direct generators of jobs during their construction and service periods, and the ways in which effective roads could indirectly stimulate employment opportunities quantitatively and, from a skills perspective, qualitatively. The economic impact analysis (ECIA) of road provision indicates the typical worker–hour requirements for the approximate estimation of employment needs. These estimates can be used whenever detailed estimates are not yet available. The estimates are provided for the construction and operation of (1) basic rural road segments, (2) bridges and viaducts, and (3) intersections, roundabouts (traffic circles), and interchanges. Where road projects contain other physical components, such as tunnels and toll plazas, the former three groups of employment rates could be used in combination with worker– hour estimates for the construction and maintenance of tunnels and tollrelated components. [ABSTRACT FROM AUTHOR]

Objective: We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. Background: Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Results: The drivers' probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. Conclusion: Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. Application: Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles. [ABSTRACT FROM AUTHOR]

Recognizing and categorizing items in weather-adverse environments poses significant challenges for autonomous vehicles. To improve the robustness of object-detection systems, this paper introduces an innovative approach for detecting objects at different levels by leveraging sensors and deep learning-based solutions within a traffic circle. The suggested approach improves the effectiveness of single-stage object detectors, aiming to advance the performance in perceiving autonomous racing environments and minimizing instances of false detection and low recognition rates. The improved framework is based on the one-stage object-detection model, incorporating multiple lightweight backbones. Additionally, attention mechanisms are integrated to refine the object-detection process further. Our proposed model demonstrates superior performance compared to the state-of-the-art method on the DAWN dataset, achieving a mean average precision (mAP) of 99.1%, surpassing the previous result of 84.7%. [ABSTRACT FROM AUTHOR]

This paper presents a simulation-driven method for assessing the safety and efficiency of traffic at roundabouts incorporating connected and automated vehicle (CAV) technology. Utilizing the newly proposed CAV-based factors specified by the Highway Capacity Manual (HCM) provided a practical framework for analyzing capacity dynamics across various traffic scenarios. Using microscopic traffic simulation on a roundabout model replicating real-world geometry and traffic attributes facilitated the identification of crucial behavioral parameters. This simulation spanned from smooth traffic scenarios to operational saturation, aiding in the study of mixed traffic scenarios during the transition to increasing CAV presence. Additionally, the study assessed the safety and traffic impact of a dedicated CAV lane using surrogate safety metrics. Aimsun software aided in model parameter calibration, which, combined with the Surrogate Safety Assessment Model (SSAM), supported safety analysis. Despite observed enhancements in roundabout performance with CAV integration, the benefits of a designated CAV lane highlighted the potential to reduce conflicts among vehicles. In conclusion, the paper emphasizes the overall performance enhancement achieved with CAVs at roundabouts while also providing insights for evaluating the potential of CAV technologies in future mobility management strategies. [ABSTRACT FROM AUTHOR]

The frequency and severity of traffic accidents are causing growing concern. This study aims to develop a tool to improve the traffic safety level on roundabouts and identify the influence of traffic operations, geometric parameters, weather, and time of day on improving roundabout traffic safety. It is the first study to evaluate the performance of the integrated Median Ranked Set Sample (MRSS) and Analytic Hierarchy Process (AHP) with statistical analysis. A hierarchy tree of accident causes has been developed using data gathered from accident reports and relevant authorities. Then, the selected stakeholders’ professionals prioritized the traffic accident causes using a MRSS and AHP. Moreover, traffic microsimulation software VISSIM was also used to extract traffic operation parameters for the analysis. Afterwards, Analysis of Variance (ANOVA) was used to validate the causes of traffic accidents. The results show that geometric design accounts for 36% of accidents at roundabouts, followed by traffic operation (22%). However, conflicting and queuing lengths are responsible for about 20% of traffic accidents. The tools developed, and the causes of accidents determined in this study will help geometric designers and city planners to take the necessary measures to minimize accidents and enhance traffic safety levels in urban areas. [ABSTRACT FROM AUTHOR]

Unsignalized roundabouts have a significant impact on traffic flow and vehicle safety. To address the challenge of autonomous vehicles passing through roundabouts with low penetration, improve their efficiency, and ensure safety and stability, we propose the proximal policy optimization (PPO) algorithm to enhance decision-making behavior. We develop an optimization-based behavioral choice model for autonomous vehicles that incorporates gap acceptance theory and deep reinforcement learning using the PPO algorithm. Additionally, we employ the CoordConv network to establish an aerial view for spatial perception information gathering. Furthermore, a dynamic multi-objective reward mechanism is introduced to maximize the PPO algorithm's reward pool function while quantifying environmental factors. Through simulation experiments, we demonstrate that our optimized PPO algorithm significantly improves training efficiency by enhancing the reward value function by 2.85%, 7.17%, and 19.58% in scenarios with 20, 100, and 200 social vehicles, respectively, compared to the PPO+CCMR algorithm. The effectiveness of simulation training also increases by 11.1%, 13.8%, and 7.4%. Moreover, there is a reduction in crossing time by 2.37%, 2.62%, and 13.96%. Our optimized PPO algorithm enhances path selection during autonomous vehicle simulation training as they tend to drive in the inner ring over time; however, the influence of social vehicles on path selection diminishes as their quantity increases. The safety of autonomous vehicles remains largely unaffected by our optimized PPO algorithm. [ABSTRACT FROM AUTHOR]

In logical terms, a 2D map is a polymorphic form of linear objects made from practical features like roads, cartos (geometrical representation of a collection of buildings or specific features), roundabouts, highways, and many more. As autonomous vehicle disrupts transportation and navigation, there is a need to detect and identify map features at a granular level with no human intervention. This paper focuses on one of the practical problems, i.e. determining whether a junction is part of roundabouts using the Map domain, which is a novel approach to the best of the knowledge of an author. In a roundabout, the junction is the node having entry or exit of roads in or away from it. Closed loops in roads are extracted by traversing each link and checking for the common junctions using a deterministic algorithm. Loops can be formed not only by roundabouts it can be present at any intersection of roads, from all the universal sets of junctions; identifying the pattern and finding the right junctions of a roundabout is a very challenging and exciting problem. The authors have solved this problem in two parts; data extraction is done by collecting all the roads of America and Europe and then features are extracted, which depend on logical and domain knowledge. Finally, for the fuzzy part of features, a machine model that provides an efficient accuracy of 81% is created as it reduces lots of manual effort for verifying and correcting junctions. This applied methodology is easy to implement. [ABSTRACT FROM AUTHOR]

With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs' capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work's current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs' capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety. [ABSTRACT FROM AUTHOR]

Transforming intersections into roundabouts has shown that a sufficient degree of road safety and traffic capacity can be achieved. Because of the lower speeds at the area of a roundabout, drivers tend to become more easily adaptive to any kind of conflict with the surrounding environment. Despite the contribution to safety, the design elements of roundabouts are not uniformly fixed on a worldwide scale because of different traffic volumes, vehicle dimensions, drivers' attitude, etc. The present study provides a brief overview of the contribution of roundabouts to road safety and the interactions between safety and the design elements of roundabouts. In addition, discussion points about current challenges and prospects are elaborated, including findings from the environmental assessment of roundabouts; their use and performance on the era of autonomous vehicles that will dominate in the near future; as well as the role and importance of simulation studies towards the improvement of the design and operation of roundabouts in favor of safer vehicle movement. The criticality of roundabouts, in terms of their geometric design as well as the provided road safety, lies upon the fact that roundabouts are currently used for the conventional vehicle fleet, which will be gradually replaced by new vehicle technologies. Such an action will directly impact the criteria for road network design and/or redesign, thereby continuously fostering new research initiatives. [ABSTRACT FROM AUTHOR]

In this paper, a structurally simple transmissive all-dielectric metamaterial absorber is designed, fabricated and measured. The unit cell consists of photosensitive resin and water, which the water layers composed of rectangle loop, rectangular block and traffic circle. It achieves over 90% absorption of electromagnetic waves in the frequency range between 11.0 and 27.3 GHz. Meanwhile, three absorption peaks are located at 12.5, 18.4, and 26.0 GHz, which the absorptive are 97.4%, 98.3%, and 98.5%, respectively. In addition, the characteristics of the designed metamaterial reveal wide-incident angle absorption and polarization-insensitive properties. The experimental sample is measured by using the free-space method, and the experimental results are verified to be in well agreement with the simulation data. Observing the distribution of electric and magnetic fields at different resonance frequencies, the physical mechanism of the absorption is attributed to strong magnetic resonance. Therefore, the proposed metamaterial has potential for widespread use in the fields of civilian and military. [ABSTRACT FROM AUTHOR]

Amidst the rising prevalence of electric vehicles (EV) and the corresponding need for adequate charging infrastructures, this study presents an innovative application of deep learning models to analyze urban traffic. Utilizing the YOLO v3 model, our research focuses on developing a prototype for counting vehicles at an urban roundabout connecting three main roads, providing essential data for planning EV charging infrastructures. This work involves adapting and optimizing the YOLO v3 model to accurately identify and count vehicles passing through the roundabout. The aim is to measure traffic volume and identify circulation trends, which are crucial for understanding the spatial and temporal distribution of vehicles. The collected data not only estimate the number of EVs likely to traverse this area but also predict the needs for charging infrastructure in and around this specific roundabout. Our prototype demonstrated high accuracy in vehicle counting, allowing detailed analysis of traffic patterns at different times of the day. This analysis reveals significant variations in traffic flows, providing valuable insights into the most suitable times and locations for installing EV charging stations. While this study focuses on a single roundabout, it establishes a precedent for using advanced deep learning techniques in assessing EV charging infrastructure needs in broader urban contexts. The results suggest that such methods can be extended to other urban areas for more comprehensive and effective planning of EV charging infrastructures, thus contributing to more sustainable and intelligent urban mobility management. [ABSTRACT FROM AUTHOR]

This study unveils insights into the spatial and social distribution of Portuguese lexical forms, or lexies, pertaining to urban life in the interior of São Paulo State. These lexies were collected through oral responses from eighty participants, elicited by nine questions addressing variants for terms such as 'traffic lights', 'speed bump', 'sidewalk', 'curb', 'traffic circle', 'plot of land', 'city bus', 'intercity bus', and 'a small grocery store where one can also drink liquor at a counter'. The analysis examines whether these lexies establish themselves as predominant norms, indicated by both consistent spatial distribution and a relative frequency exceeding 50% across the localities surveyed. Out of the nine cases studied, eight meet this criterion, with the following overall relative frequencies: 'semáforo' 53%; 'lombada' 96%; 'calçada' 94%; 'guia' 84%; 'rotatória' 74%; 'terreno (baldio)' 79%; 'ônibus' (in the sense of 'city bus') 71%; 'ônibus' (in the sense of 'intercity bus') 51%. The ninth variant, 'bar,' nearly meets this standard as well, with a relative frequency of 41% just falling short of the minimum threshold. [ABSTRACT FROM AUTHOR]

The article focuses on the protagonist's tendency to ponder unconventional and seemingly trivial questions, leading to conflicts in her relationship, particularly with her husband Abdul, who expresses concern about the impact of her thoughts on their family dynamic and the space-time continuum. Topics include the protagonist's inner conflicts, Abdul's frustration with her unconventional thinking, and the tension between motherhood and personal intellectual pursuits.

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We propose a multivariate Grey-Markov model to quantify traffic accident risk from different causality factors in roundabouts that is uniquely suited for the scarce and stochastic traffic crash data from roundabouts. A data sample of traffic crashes occurring in roundabouts in the U.S. State of Michigan from 2016 to 2021 was collected to investigate the capabilities of this modeling methodology. The multivariate grey model (MGM(1,4)) was constructed using grey relational analysis to determine the best dimensions for model optimization. Then, the Markov chain is introduced to address the unfitness of stochastic, fluctuating data in the MGM(1,4) model. Finally, our proposed hybrid MGM(1,4)-Markov model is compared with other models and validated. This study highlights the superior predictive performance of our MGM(1,4)-Markov model in fore-casting roundabout traffic accidents under data-limited conditions, achieving a 3.02% accuracy rate, in contrast to the traditional GM(1,1) model at 8.30% and the MGM(1,4) model at 4.47%. Moreover, incorporating human, vehicle, and environmental risk factors into a multivariate crash system yields more accurate predictions than merely aggregating crash counts. [ABSTRACT FROM AUTHOR]

• The effects of RTL's geometric factors on crash frequency vary based on location and time. • Traffic roundabouts increase crashes by 65% more than intersections in urban counties. • RTLs in Southern Indiana have more crashes on snow days than in the North. • Exclusive RTLs on county roads decrease 14% of crashes more than the shared RTLs. • Increasing RTL's length increase crashes for RTLs on interstate and state roads. Introduction : Right-turn lane (RTL) crashes are among the key contributors to intersection crashes in the US. Unfortunately, the lack of deep insights into understanding the effects of RTL geometric design factors on crash frequency impedes improving RTL safety performance. Method : Taking the crash data in ten counties in Indiana state from 2013 to 2016 as a case study, this study investigates the safety performance of RTL geometric configuration based on multi-sources. We introduce the geographically and temporally weighted negative binomial model (GTWNBR) to capture the space and time instability in crashes. Results : The results show that the impacts of RTL geometric design factors on crash frequency vary significantly among space and time. Several key insights can be obtained from the state-wide and multi-years crash analysis by associating the estimated parameters with road classes, localities, and counties. Conclusions : First, the RTL's length, width, turning radius, and the installments of traffic roundabouts present higher spatiotemporal heterogeneity than other factors in modeling the crash frequency. Second, the effects of RTL's geometric factors vary significantly across space and time. The presence of bicycle and pedestrian lanes is more likely to increase crashes in urban areas than in rural ones, especially at nighttime. Third, while exclusive RTLs decrease the crash frequency compared to the shared RTLs, the exclusive RTLs are more likely to increase the crashes for RTLs on the county road than on other road classes. Increasing RTL's turning radius and decreasing RTL's length is more likely to promote crashes for RTLs on county roads than on other road classes. Practical Applications : The insights provide vital guidance to improve the safety performance of geometric configuration for RTLs and intersections. [ABSTRACT FROM AUTHOR]

Our calculations of the global instability index (G) values for some diamond‐like materials with the general formula I2–II–IV–VI4 have indicated that the structures may be unstable or incorrectly determined. To compute the G value of a given compound, the bond valence sums (BVSs) must first be calculated using a crystal structure. Two examples of compounds with high G values, based on data from the literature, are the wurtz–stannite‐type dicopper cadmium silicon tetrasulfide (Cu2CdSiS4) and the stannite‐type dicopper mercury tin tetrasulfide (Cu2HgSnS4), which were first reported in 1967 and 1965, respectively. In the present study, Cu2CdSiS4 and Cu2HgSnS4 were prepared by solid‐state synthesis at 1000 and 900 °C, respectively. The phase purity was assessed by powder X‐ray diffraction. Optical diffuse reflectance UV/Vis/NIR spectroscopy was used to estimate the optical bandgaps of 2.52 and 0.83 eV for Cu2CdSiS4 and Cu2HgSnS4, respectively. The structures were solved and refined using single‐crystal X‐ray diffraction data. The structure type of Cu2CdSiS4 was confirmed, where Cd2+, Si4+ and two of the three crystallographically unique S2− ions lie on a mirror plane. The structure type of Cu2HgSnS4 was also verified, where all ions lie on special positions. The S2− ion resides on a mirror plane, the Cu+ ion is situated on a fourfold rotary inversion axis and both the Hg2+ and the Sn4+ ions are located on the intersection of a fourfold rotary inversion axis, a mirror plane and a twofold rotation axis. Using the crystal structures solved and refined here, the G values were reassessed and found to be in the range that indicates reasonable strain for a stable crystal structure. This work, together with some examples gathered from the literature, shows that accurate data collected on modern instrumentation should be used to reliably calculate BVSs and G values. [ABSTRACT FROM AUTHOR]

The Guangdong-Hong Kong-Macao Greater Bay Area (the Greater Bay Area, the GBA) aims to facilitate in-depth cooperation among the Guangdong, Hong Kong, and Macao regions to create a world-class bay area-based urban agglomeration. This endeavor seeks to guide China's economy toward high-quality development and comprehensive opening-up. This paper examines the challenges and obstacles faced by Guangdong, Hong Kong, and Macao regarding regional coordinated development, thus putting forward the following standpoints. It proposes enhancing infrastructure interconnectivity to establish the GBA 1-hour Express Traffic Circle, thereby driving the construction of a world-class international shipping and logistics hub. Furthermore, the paper advocates for establishing an industrial collaborative development system in the GBA, jointly creating a pilot zone for industrial transformation and upgrading, and further advancing market integration. Creating an international innovation corridor and innovation center for science and technology (S&T) industry is also essential. This initiative would center around critical sectors, improving S&T innovation chain, and building a talent cooperation demonstration zone. The paper also emphasizes the establishment of a transport and logistics hub for the Belt and Road Initiative, along with the joint development of a cultural, economic, and trade exchange platform under the "Belt and Road" framework. Leveraging the comparative advantages of the three regions, the paper underscores the importance of innovative collaboration models, fostering a mutually beneficial and complementary mechanism for synergistic development. These approaches aim to realize the integrated and innovative development of the GBA. [ABSTRACT FROM AUTHOR]

Non-Playable Characters (NPCs) are essential for creating a fully immersive video game experience. However, their behaviours are typically programmed using rigid methods such as Finite State Machines and Behaviour Trees. These methods are hard to maintain, struggle with implementing intricate cooperative behaviours, and patterns in NPC behaviour can easily be exploited by human players. Machine Learning and Reinforcement Learning (RL) methods in particular, offer a solution to these challenges by allowing dynamic and real-time NPC responses to human player actions and other characters in the game. This work describes and extends upon a Curriculum-based RL approach to train a single model for multiple agents representing vehicles to respect traffic regulations. These vehicles learn to navigate a road network which includes a traffic lights crossroad intersection and a roundabout junction. A comparison of our simulated data against datasets obtained from real world data is conducted; results demonstrate that the learned models have similar trends to the real-world data. • Multi-agent simulation for game characters navigating a road network. • Reinforcement Learning in game design can generate compelling agent behaviours. • Curricula can help reinforcement learning process to learn complex behaviours. • Macroscopic behaviour of agents matches real world data. [ABSTRACT FROM AUTHOR]

Copyright of Pós: Revista do Programa de POS-Graduacao Em Artes - EBA/UFMG is the property of Pos - Programa de Pos-graduacao em Artes (PPG-Artes) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The collaborative strategy of vehicle-road-environment based on intelligent and connected vehicles (ICVs) to assist in driving vehicles safely and relieve traffic congestion has become an effective solution. This paper proposed a strategy for vehicle lane change and roundabouts traffic based on vehicle profile (VP) in combination with the driving characteristics of roundabouts. Initially, in order to solve the confusion problem of multisource heterogeneous data of ICVs in roundabouts, this paper defines VP to describe and characterize the multidimensional data of ICVs, so the data in ICVs can be further applied. Furthermore, the weights of relevant parameters in the VP are updated based on the random forest algorithm. In addition, the payoff function is designed for the lane change decision at the exit of roundabouts based on the VP and dynamic weights. Finally, the performance of the proposed algorithm is compared with other algorithms through the SUMO platform and three scenarios are used in the simulation verification, including traffic congestion, normal, and sparse. The experimental results verify the optimization effect of vehicle profile on roundabout traffic strategy and also show that this algorithm can effectively improve the efficiency of vehicle traffic in roundabouts. In particular, the efficiency and comfort of vehicles in roundabouts are effectively improved in normal traffic scenarios. [ABSTRACT FROM AUTHOR]

Purpose: The rapid development of three-dimensional (3D) printing makes it familiar in daily life, especially the fused deposition modeling 3D printers. The process planning of traditional flat layer printing includes slicing and path planning to obtain the boundaries and the filling paths for each layer along the vertical direction. There is a clear division line through the whole fabricated part, inherited in the flat-layer-based printed parts. This problem is brought about by the seam of the boundary in each layer. Hence, the purpose of this paper is to propose a novel helical filling path generation with the ideal surface-plane intersection for a rotary 3D printer. Design/methodology/approach: The detailed algorithm and implementation steps are given with several worked examples to enable readers to understand it better. The adjacent points obtained from the planar slicing are combined to generate each layer's helical points. The contours of all layers are traversed to obtain the helical surface layer and helical path. Meanwhile, the novel rotary four-degree of freedom 3D printer is briefly introduced. Findings: As a proof of concept, this paper presents several examples based on the rotary 3D printer designed in the authors' previous research and the algorithms illustrated in this paper. The preliminary experiments successfully verify the feasibility and versatility of the proposed slicing method based on a rotary 3D printer. Originality/value: This paper provides a novel and feasible slicing method for multi-axis rotary 3D printers, making manufacturing thin-wall and complex parts possible. To further broaden the proposed slicing method's application in further research, adaptive tool path generation for flat and curved layer printing could be applied with a combination of flat and curved layers in the same layer, different layers or even different parts of structures. [ABSTRACT FROM AUTHOR]

The article discusses feasible treatments for improving the performance of undivided four-lane urban roads, which suffer from poor capacity, pedestrian service, and safety, suggesting 15 potential treatments including restriping, and redirecting movements as alternatives to road diets.

The events leave a lot of repercussions on city's architecture as a representation of the stations of ideological change reflected in what is happening to society in economic, political, scientific and social changes. After 2003 events in An-Najaf Al-Ashraf city had a great impact on its architecture resulted by the effect of continuous transformations during the past period in all the levels, as well as the progress, technological development and sudden openness to the west world. Therefore, the lack of contemporary knowledge of the strategies role that used for reconstruction in the emergence of architectural production during the past two decades in its current form, the research problem came as follows (the lack of knowledge of the strategy of disorder within the entrances to new democracy and the loss of local capabilities to observe its impact on architecture in the city of Najaf after 2003). The purpose of this study (investigating the effect of the strategy of chaos associated with the events following 2003 on architectural output in An Najaf city, and proposing a method of achieving local identity continuity in the city and its communication with residents). The conclusions of the study were appropriate for proposing a strategy for the reconstruction of the city and the continuation of its construction through the use of the field survey method. This was accomplished by distributing a questionnaire to a sample of architects in the city of Najaf, as they are familiar with the movement of architecture in the city. The concept of the study of disorganization strategy had a definitive conceptualization of its impact on the architecture of the holy city of Najaf, along with a proposal of the blue-green infrastructure strategy as a suitable strategy for the reconstruction of the city. [ABSTRACT FROM AUTHOR]

Roundabouts are a common traffic infrastructure, which are supposed to facilitate safe and smooth traffic flow. Electric bikes as a common traffic tool in a lot of cities in China play an important role in relieving traffic congestion due to the rapid increase of motor vehicles on roads. However, compared with cyclists, e-bikers are more vulnerable because of their higher speed when colliding with motor vehicles. In this research, the spatial-temporal distribution characteristics of conflicts between motor vehicles and electric bikes (e-bikes) at signalized roundabouts are explored. First, the time distance method was used to identify conflicts, and the time to collision (TTC) was selected as the discrimination index we proposed for two representative conflict types. On conflict heat maps and conflict distribution during one signal cycle, we found a series of spatial-temporal conflict distribution regularities. Spatially, the proposed two representative types of conflicts were mainly distributed at exit areas and near the outermost circulatory lanes. Conflict Type 1 was mainly distributed around the outermost motor vehicle lanes, and Type 2 was mainly distributed inside the outermost vehicle lanes and behind the second stop line. In time span, both types of conflicts showed rapid increase before reaching a peak at 30 percentile green time and then decreasing gradually after that peak. Type 1 presented a sharp increasing range during 0–10 percentile green time, while the sharp increasing range for Type 2 presented during the 10–20 percentile. The conclusions developed by this article could provide a theoretical basis for improving traffic safety at roundabouts. [ABSTRACT FROM AUTHOR]

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With the rapid development of high-speed railway (HSR) transportation in China, its impact on regional spatial patterns and shaping has become increasingly significant. This study took seven urban agglomerations in the Yellow River Basin as the research object, using the 2 h HSR access time in the Yellow River Basin to comparatively analyze the differences in HSR access in the urban agglomeration in the Yellow River Basin, and using the 3 h HSR access to central cities as the background to conduct regional division and overlapping space identification through cross-regional economic links, before finally selecting the overlapping city of Changzhi for long-term space development strategic planning. The main conclusions were as follows: First, the low-value area of HSR travel time in the Yellow River Basin urban agglomerations was biased toward the center of the urban agglomerations, while the peripheral areas were relatively high-value travel traffic circles, and the HSR travel time showed a circular spatial pattern characteristic of continuous expansion from the center to the peripheral areas. Four urban agglomerations in the upper reaches of the city achieved a 2 h access pattern within the urban agglomeration, whereas three urban agglomerations in the middle and lower reaches of the city only reached the 2 h access level in the center. Second, the Yellow River Basin was divided into six community spaces using the SLPA model based on the economic linkage between the central city and other cities, which were filtered by the 3 h access time from the central city to each city for HSR travel. Three of the six communities produced overlapping spaces, i.e., Community 3 and Community 4 produced overlapping spaces containing Linfen, Community 3 and Community 5 produced overlapping spaces containing Changzhi, Handan, and Xingtai, and Community 4 and Community 5 produced overlapping spaces containing Yuncheng and Sanmenxia. Third, the overlapping space of Changzhi City was selected as a case study for a visionary strategic planning outlook. Combining the geographic location characteristics and future development opportunities of Changzhi, we can try to transform a pass-through node like Changzhi into a hub node in the future, strengthening the gateway status and expanding the hinterland. According to the results of the research and analysis, policymakers can try to implement the expansion and renovation of HSR trunk lines, break the transportation bottlenecks in less developed areas, improve the coverage of the HSR network, and establish a "cross-urban agglomeration" cooperation and coordination mechanism. [ABSTRACT FROM AUTHOR]

Connected vehicles (CVs) have led to innovative changes in the transportation field. In this wise, an extended CA model was established incorporating the CVs and regular vehicles to investigate a roundabout traffic flow under the open boundary condition. The throughput and the phase diagrams of the system in the ( α 1 , α 2 ) area is constructed in both cases: with and without CVs. The density profile and the capacity are also calculated. The simulation findings show that CVs presentation changes the traffic dynamics, enhancing roundabout capacity in accordance with the CVs penetration rate. Furthermore, it is found that roundabout size plays a dominant role in its performance where it suggests that the midsized roundabouts operate more optimally. Another relevant aspect is the short sized of Splitter Island shows a better performance. Moreover, the traffic dynamics is negatively (resp. positively) correlated to the probability of displaying indicator P ind as well as the probability of aggressive driving behavior of regular vehicles (resp. the cooperation probability P cop ). [ABSTRACT FROM AUTHOR]