Microscopic Simulation Research Articles

Wettability-Oriented Laser Microgrooving Process on Cemented Carbide Surface.

Surface micro-texture has been shown to enhance wettability and reduce wear on cutting tools. However, there is limited research on how laser parameters impact the dimensional accuracy of surface texture and its wettability. This study focuses on producing arrayed groove textures on WC/Co cemented carbide surfaces using Nd: YAG laser, evaluating the effect of the laser parameters on surface topography and texture accuracy through microscopic observation and simulation. The results indicate that, with laser parameters such as a number of passes less than 5, approximately 16 W power, scanning speed of 100-150 mm/s, and pulse frequency of 30 kHz, the error between the groove width and laser spot diameter was 4.7%. Additionally, the study explores the impact of the groove texture on surface wettability using the solid droplet method and XPS analysis. Comparative experiments reveal that increased surface roughness enhanced oleophobicity, with surfaces exhibiting high texture accuracy and integrity showing improved oleophobic and spreading properties. Thus, the precise regulation of laser processes is crucial for maintaining surface texture integrity and enhancing surface wettability.

Microscopic simulation of bicycle traffic flow incorporating cyclists’ heterogeneous dynamics and non-lane-based movement strategies

Cycling as a mode of transport is on an upward trend as a low-emission alternative to driving in urbanized areas nowadays. With the increasing number of cyclists, it is of great importance to assess the capacity of cycling infrastructure in practice. Simulation models are useful tools to investigate bicycle flow performance considering cyclists’ distinct moving behaviors. However, existing bicycle simulation models are restricted by either space discretization, lane-based setup, adaptation from models for car traffic, or complicated calibration requirement in a force-based environment. In addition, cyclists’ decision-making ability in the operational-level cycling behavior are not well-captured in these models. This paper proposes a comprehensible microscopic bicycle simulation model which includes a detailed decision-making process and the ability to simulate continuous-space lateral movement. The model consists of three levels, maneuver decision, movement planning, and physical acceleration. It is able to simulate bicycle flow dynamics in undersaturated traffic conditions on an exclusive bike path. As we do not intend to show the empirical validity of the proposed model, the simulation experiment aims at verifying the model and exploring bicycle flow performance in various scenarios by estimating the fundamental diagrams (FDs). The effect of different path widths on bicycle flow capacity is first explored. Other behavioral factors, including desired speed heterogeneity, overtaking incentive, and safety region size perceived by cyclists, which can potentially influence the shape of the FD are also tested. The model can be further extended to simulate relatively complex cycling behavior with cooperative and anticipative strategies and investigate bicycle flow characteristics in congested traffic conditions.

Occurrence and Flow Behavior for Oil Transport in Mixed Wetting Nanoscale Shale Bedding Fractures.

Shale reservoirs are characterized by an abundance of nanoscale porosities and microfractures. The states of fluid occurrence and flow behaviors within nanoconfined spaces necessitate novel research approaches, as traditional percolation mathematical models are inadequate for accurately depicting these phenomena. This study takes the Gulong shale reservoir in China as the subject of its research. Initially, the unique mixed wetting characteristics of the Gulong shale reservoir are examined and characterized using actual micropore images. Subsequently, the occurrence and flow behavior of oil within the nanoscale bedding fractures under various wettability scenarios are described through a combination of microscopic pore image and molecular dynamics simulations. Ultimately, a mathematical model is established that depicts the velocity distribution of oil and its apparent permeability. This study findings indicate that when the scale of the shale bedding fractures is less than 100 nm, the impact of the nanoconfinement effect is significant and cannot be overlooked. In this scenario, the state of oil occurrence and its flow behavior are influenced by the initial oil-wet surface area on the mixed wetting walls. The study quantifies the velocity and density distribution of oil in mixed wetting nanoscale shale bedding fractures through a mathematical model, providing a crucial theoretical basis for upscaling from the nanoscale to the macroscale.

Evaluating the Performance of Right Turn Lanes at Signalized Intersection Using Traffic Simulation Model

The issue of traffic congestion at signalized intersections is a concern in transportation systems due to the growth of urban areas and increased vehicular transportation. To study the evolution of congestion and evaluate the traffic performance operation of signalized intersections under problematic congested and improved conditions, the microscopic simulation VISSIM software is utilized. The objectives of this paper are to evaluate operational techniques, build a simulation model, and produce a well-calibrated and validated model. The methodology procedure to evaluate the signalized intersection involves the application of a traffic simulation model to observe real-time delays and stopped vehicles. Using the VISSIM software Version 9 to create an intersection model and redesign geometry with an exclusive right turn to enhance the intersection functionality and reduce delay. Our research focused on the Al-Nakhala signalized intersection located in the southern part of Palestine urban street in Baghdad city. This intersection is one of the busiest along the corridor due to significant land-use changes in the study area, including residential, educational, or commercial areas generating daily pressure from additional trips and saturating the absorptive capacity of the intersections. The proposed scenario of an exclusive right–turn could reduce the queue length and vehicle delay at the signalized intersection, resulting in a more efficient traffic operation. As a result of the reduction in vehicle delay, the Level of service (LOS) for the north, west, and east approaches improved from F to D. However, there was only a slight improvement for the south approach, with the LOS changing from E to F. Nonetheless, there was a noticeable reduction in queue length and vehicle delay ranging from 25% to 50%. Doi: 10.28991/CEJ-2024-010-07-010 Full Text: PDF

SAFETY AND OPERATIONAL ASSESSMENT OF COOPERATIVE DRIVING SYSTEMS ON ROUNDABOUTS

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.

A Study on the Estimation of Change in Traffic States at Unsignalized Intersections based on the Mixed Rate of Autonomous Vehicle Using Microscopic Traffic Simulation

A Study on the Estimation of Change in Traffic States at Unsignalized Intersections based on the Mixed Rate of Autonomous Vehicle Using Microscopic Traffic Simulation

Anisotropic Icephobic Mechanisms of Textured Surface: Barrier or Accelerator?

Icing has been seen as an economic and safety hazard due to its threats to aviation, power generation, offshore platforms, etc., where passive icephobic surfaces with a surface texturing design have the potential to address this problem. However, the intrinsic icephobic principles associated with the surface textures, energy, elasticity, and hybrid effects are still unclear. To explore the anisotropic wettability, ice nucleation, and ice detaching behaviors, a series of textured poly(dimethylsiloxane) (PDMS)-based coatings with various texture orientations were proposed through a simple stamping method with surface functionalization. The anisotropic hydrophobic/icephobic phenomena and mechanisms were discovered from wettability evaluation, experimentally studied by icing/deicing experiments, and finally verified by microscopic numerical simulations. One-way analysis of variance (one-way ANOVA analysis) was used to analyze the effect of surface textures on hydrophobic/icephobic properties, which assisted in understanding anisotropic phenomena. Typical anisotropic ice nucleation and growth on the textured coatings were clarified using in situ environmental scanning electron microscope (ESEM) characterization. The ice/coating interfacial stress responses were studied by numerical stimulation at the microscopic level, further verifying the localized, amplified, and propagated stress at the ice/coating interface. The theoretical anisotropic responses, barrier effect, and accelerating effect were verified to interpret the anisotropic wettability and icephobicity, depending on the specific surface conditions. This study revealed the basics of the anisotropic icephobic mechanisms of textured icephobic surfaces, further facilitating the R&D of passive icephobic surfaces.

Impact of navigation apps on congestion and spread dynamics on a transportation network

In recent years, the widespread adoption of navigation apps by motorists has raised questions about their impact on local traffic patterns. Users increasingly rely on these apps to find better, real-time routes to minimize travel time. This study uses microscopic traffic simulations to examine the connection between navigation app use and traffic congestion. The research incorporates both static and dynamic routing to model user behavior. Dynamic routing represents motorists who actively adjust their routes based on app guidance during trips, while static routing models users who stick to known fastest paths. Key traffic metrics, including flow, density, speed, travel time, delay time, and queue lengths, are assessed to evaluate the outcomes. Additionally, we explore congestion propagation at various levels of navigation app adoption. To understand congestion dynamics, we apply a susceptible–infected–recovered (SIR) model, commonly used in disease spread studies. Our findings reveal that traffic system performance improves when 30–60% of users follow dynamic routing. The SIR model supports these findings, highlighting the most efficient congestion propagation-to-dissipation ratio when 40% of users adopt dynamic routing, as indicated by the lowest basic reproductive number. This research provides valuable insights into the intricate relationship between navigation apps and traffic congestion, with implications for transportation planning and management.

Simulation-Oriented Analysis and Modeling of Distracted Driving

Distracted driving significantly affects the efficiency and safety of traffic flow. Modeling distracted driving behavior in microscopic traffic flow simulation is essential for understanding its critical impacts on traffic flow. However, due to the influence of various external factors and the considerable uncertainties in behavior characteristics, modeling distracted driving behavior remains a challenge. This study proposed a model which incorporates distraction features into the microscopic traffic flow model to simulate distracted driving behavior. Specifically, the study first examines the characteristics of distracted driving, including the intervals and durations of distraction events, as well as the patterns and environments of distraction. It then introduces distraction parameters into the Intelligent Driver Model (IDM), including reaction time delays and perception deviations in both speed difference and following distance. These parameters are quantified by probabilistic distributions to reflect the uncertainty and individual differences in driving behavior. The model is calibrated and validated using 772 distracted following events from the Shanghai Naturalistic Driving Study (SH-NDS) data. Three patterns of distraction (excessive, moderate, mild) are distinguished and modeled separately. The results show that the model’s accuracy surpasses that of the IDM under various road types and traffic volumes, with an average improvement in model accuracy of about 11.30% on expressways with high traffic volume, 4.54% on expressways with low traffic volume, and 4.46% on surface roads. Meanwhile, the model can effectively simulate the variations in reaction times and perceptual deviations in both speed and following distance for different distraction modes at the individual level, maintaining consistency with reality. Finally, the study simulates distracted driving behavior under different road environments and traffic volumes to explore the impact of distracted driving on traffic flow. The simulation results indicate that an increase in the proportion of distraction reduces the efficiency and safety of traffic flow, which is consistent with real-world observations. Since the model considers human distraction factors, it can generate more dangerous driving scenarios in simulations, which holds significant importance for safety-related research. The findings from this study are expected to be helpful for understanding distracted driving behavior and mitigate its negative influence on the efficiency and safety of traffic flow.

Safety evaluation via conflict classification during automated shuttle bus service operations

The widespread adoption of Connected and Automated Vehicles (CAVs) is being propelled, not only in the realm of private vehicles but also within transit systems. This development serves to enhance urban transport activities, rendering transportation more appealing to passengers. The present study aims to identify and examine the safety effects of testing different operational speed shuttle bus services in various future mobility conditions. To investigate impacts of autonomous shuttle bus services and to further examine their operational speed, the microscopic simulation method was performed. Specifically, four sets of simulation scenarios were comprised: a baseline scenario representing the current conditions and three operational speed scenarios (15 km/h, 30 km/h and 45 km/h) for an autonomous shuttle service. Each one of these sets included eleven CAV market penetration rates (MPRs) of CAVs of the general traffic (ranging from 0 to 100% in 10% increments). By analyzing the trajectory data extracted from microsimulation, traffic conflicts were identified and further analyzed by developing Mixed-Effects Multinomial Logit Regression models (ME-MLMs) in order to associate conflict type taking into account network characteristics as well as traffic conditions. Several aspects were determined as statistical significant parameters influencing type of conflict. The analysis yielded several significant findings that provide quantitative measurements and assessments of the effects observed, enabling a better understanding of the safety implications associated with the widespread adoption of automated services.

CycLight: Learning traffic signal cooperation with a cycle-level strategy

This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Traditional RL-based step-by-step traffic signal controllers, while precise and responsive, demand high computational resources for frequent data exchanges and can increase traffic accident risks due to disordered phase switching. Different from the step-by-step control strategy aforementioned, the proposed CycLight adopts a cycle-level strategy, optimizing cycle length and splits simultaneously using Parameterized Deep Q-Networks (PDQN) algorithm. This cycle-level approach effectively reduces the computational burden associated with frequent data communication, meanwhile enhancing the practicality and safety of real-world applications. Moreover, a decentralized framework is formulated for multi-agent cooperation, while attention mechanism is integrated to accurately assess the impact of the surroundings on the current intersection. CycLight is tested in a large synthetic traffic grid using the microscopic traffic simulation tool, Simulation of Urban MObility (SUMO). Experimental results not only demonstrate the superiority of CycLight over other state-of-the-art approaches but also showcase its robustness against information transmission delays.

Innovative microscope simulator for cataract patients: Enhancing understanding and comfort.

Innovative microscope simulator for cataract patients: Enhancing understanding and comfort.

Study on the size effect of dynamic tensile strength in lightweight concrete using Leca aggregates at the mesoscopic level

To investigate the influence of volume fraction of lightweight aggregate concrete on dynamic tensile strength and size effect, theoretical derivation and microscopic numerical simulation are combined in this study, and a dynamic hybrid fracture cohesive zone constitutive model for lightweight aggregate concrete to characterize the tensile behavior considering strain rate at microscopic scale. The research reveals that, for geometrically similar specimens of various sizes, the direct tensile strength with different aggregate volume fractions exhibits differing degrees of improvement with increasing strain rates. The tensile strength gradually decreases as volume fraction increases. The established coupling function incorporating strain rate, specimen size, and aggregate volume fraction accurately characterizes the dynamic tensile strength variation. In addition, a theoretical framework is proposed to provide a microscopic understanding of the static-dynamic size effect. This framework facilitates the estimation of the dynamic tensile strength under specific volume fraction, specimen size and strain rate conditions.

Ultrafast Coherent Exciton Couplings and Many-Body Interactions in Monolayer WS2.

Transition metal dichalcogenides (TMDs) are quantum confined systems with interesting optoelectronic properties, governed by Coulomb interactions in the monolayer (1L) limit, where strongly bound excitons provide a sensitive probe for many-body interactions. Here, we use two-dimensional electronic spectroscopy (2DES) to investigate many-body interactions and their dynamics in 1L-WS2 at room temperature and with sub-10 fs time resolution. Our data reveal coherent interactions between the strongly detuned A and B exciton states in 1L-WS2. Pronounced ultrafast oscillations of the transient optical response of the B exciton are the signature of a coherent 50 meV coupling and coherent population oscillations between the two exciton states. Supported by microscopic semiconductor Bloch equation simulations, these coherent dynamics are rationalized in terms of Dexter-like interactions. Our work sheds light on the role of coherent exciton couplings and many-body interactions in the ultrafast temporal evolution of spin and valley states in TMDs.

Towards a fundamental understanding on Microscopic Transportation Simulation of Autonomous Delivery Robots

A large number of studies were focused into the adoption of ADRs in the current transportation and logistics, safety in practical applications, and deployment of advanced robotic technology. Only a few contributed to the simulation of microscopic transportation for autonomous delivery robots. Many researchers emphasized the importance of overcoming modelling gaps and covering the scope of simulation as realistically as possible in order to obtain reliable results from the simulation. The versatility of the technology, the nature of the logistics and components of ADR in the real world, and other challenges present challenges to the transportation modeller when it comes to thinking about the possible representation of ADRs in the simulation environment. The gradual development of both computational capacity and underlying behavioural patterns in microscopic traffic simulators opens the door for researchers to develop realistic ADRs model in the microscopic transportation simulation to investigate road interaction and the resulting impacts. This review explored and organized the currently available technologies, operational methods, and simulation components that need to be considered when modelling ADRs in microscopic traffic simulation. Furthermore, this review provides a basic understanding for modellers to address the potential challenges to cope on recent technological developments in ADR modelling and simulation.

Modelling two-dimensional driving behaviours at unsignalised intersection using multi-agent imitation learning

Traffic at unsignalised intersection usually involves complex interactions. It is critical to explicitly model the two-dimensional driving behaviours and capture the interactions among vehicles. However, little effort has been made to develop microscopic traffic simulation models at unsignalised intersections, which can generate realistic vehicle trajectories. To fill this gap, this study aims to develop a two-dimensional data-driven simulation model at an unsignalised intersection based on multi-agent imitation learning for generating realistic trajectories of vehicles crossing the intersection and macroscopic traffic characteristics. We propose a multi-agent adversarial inverse reinforcement learning model with four policies (MA-AIRL-4) to separately learn the driving behaviours with different directions (East-bound, West-bound, South-bound, and North–bound) by capturing the interactions between the vehicles. Using the vehicle trajectories data extracted from an unsignalised intersection of the open-source Interaction Dataset, we evaluate the performance of the proposed model by comparing it with several bench-marking models, i.e., MA-AIRL-2 model with two policies (i.e., one policy for West-East and North-South direction, respectively), MA-AIRL-1 model with one policy for all vehicles, three corresponding multi-agent generative adversarial imitation learning (MA-GAIL) models with four policies, two policies and one policy, respectively, and long short-term memory (LSTM) model. The results demonstrate the superiority of the proposed MA-AIRL-4 model in generating accurate vehicle trajectories and reproducing realistic speed distributions. The interpretation of the recovered reward function also indicates the effective learning of the proposed model and provides insights into the learned behaviours.

Study of the mechanical properties and propagation mechanisms of non-coplanar and discontinuous joints via numerical simulation experiments

Non-coplanar and discontinuously jointed rock masses are more complex than coplanar and discontinuously jointed rock masses. The mechanical properties and propagation mechanisms of non-coplanar and discontinuous joints were studied via direct shear tests with microscopic numerical simulation experiments. The numerical simulation tests were performed under different normal stresses, joint inclination angles, and shear rates. The numerical experimental results show that the microscale failure of non-coplanar and discontinuously jointed rock masses is mainly caused by tensile cracks. Additionally, when the peak shear stress is reached, the growth rate of cracks increases rapidly, and the number of cracks increases with increasing normal stress. The shear strength of non-coplanar and discontinuously jointed rock masses increases with increasing normal stress. Under the same normal stress, the variation curves of the shear strength of non-coplanar and discontinuously jointed rock masses with respect to the dip angle exhibit an “S”-shaped nonlinear pattern. Rock masses with joint inclination angles of approximately 15° and 65° have minimum and maximum shear strengths, respectively. The joint dip angle has a significant impact on the final failure mode of rock bridges in the rock mass. As the joint dip angle increases, the final failure modes of rock bridges change from “end-to-end” connection to a combination of “head-to-head” and “tail-to-tail” connections. The shear rate has a certain impact on the peak shear stress, but the impact is not significant. The spatial distribution of the tensile force chains changes as shearing progresses, and stress concentration occurs at the tips of the original joints, which is the reason for the development of long tensile cracks in the deeper parts.

Modeling coupled driving behavior during lane change: A multi-agent Transformer reinforcement learning approach

In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles. The interactions not only affect their driving behaviors but also influence the traffic flow. This study aims to model the coupled behavior of the lane changer and the follower in the target lane during LC. Large-scale real-world connected vehicle (CV) data from the Safety Pilot Model Deployment (SPMD) program are used to extract LCs and study vehicle interactions. A multi-agent Transformer-based deep deterministic policy gradient (MA-TDDPG) method is proposed to model the coupled behaviors during LC. The multi-agent framework can handle the multiple agents’ behaviors with interactions, and the Transformer can process the observation-action memory accurately and efficiently. The MA-TDDPG algorithm can learn the sequential decision-making process over continuous action space during LC with the accommodation of the multi-vehicle interaction and the driver’s memory effect. Compared to traditional supervised learning and reinforcement learning methods, it demonstrates superior performance in imitating the longitudinal and lateral actions of the lane changer and the follower. The findings of this study provide insights into the development of microscopic simulations by producing realistic LC behaviors, and assistance/automation LC systems by generating LC motions and responses conforming to human driving habits. The model also creates an interactive simulation environment and lays the foundation for optimizing driving strategies.

Analysis of user behavior in urban parking under different level of information scenarios provided by smart devices or connected cars

Due to the issues of land redevelopment and changes of use within urban areas, many cities must adopt measures to reorganise and optimise parking space. This paper proposes a methodology to study one of them by implementing parking information systems (PIS). This solution offers users a competitive advantage by allowing them to know about the free parking spaces at the moment of decision-making. To achieve this goal, microscopic simulations are conducted to analyse the effects of various scenarios involving the implementation of PIS. The data used in these simulations is obtained from the Santander area in Spain. For the evaluation of results, a methodology has been developed that combines the evaluation of social factors for citizens and operational impacts for decision-makers. The results show significant improvements with increasing user information rate, e.g., the number of unsuccessful parking attempts before finding a final parking space is reduced by 55%, and 37% less particulate pollutants are emitted into the atmosphere.

ENHANCE: Multilevel Heterogeneous Performance-Aware Re-Partitioning Algorithm For Microscopic Vehicle Traffic Simulation

Driven by our work on a large-scale distributed microscopic road traffic simulator, we present ENHANCE, a novel re-partitioning approach that allows incorporating fine-grained simulator-specific cost models into the partitioning process to account for the actual performance characteristics of the simulator. The use of explicit cost models enables partitioning for heterogeneous resources, which are a common occurrence in cloud deployments. Importantly, ENHANCE can be used in conjunction with other partitioning approaches by further enhancing partitions according to provided cost models. We demonstrate the benefits of our approach in an experimental evaluation showing performance improvements of up to 29% against METIS under heterogeneous conditions. Taking a different perspective, the partitioning produced by ENHANCE can provide similar performance as METIS, but using up to 20% fewer resources.