Merging Behavior Research Articles

Merging behaviour and fatigue life evaluation of multi-cracks in welds of OSDs

Experimental studies were conducted to investigate the merging behaviour of multiple fatigue cracks (MFCs) in rib-to-deck welds of orthotropic steel decks (OSDs). Numerical simulations were conducted to investigate the mechanism of the merging behaviour of MFCs on fatigue life. In addition, the sensitive parameter in the MFCs affecting the fatigue life were revealed. Finally, a comprehensively failure criterion for MFCs was proposed. Results show that the merging behaviour of MFCs can be effectively observed by the beach marks on the fracture surface of the rib-to-deck weld. The merging behaviour is a key factor driving cracks penetrating through the deck, which significantly increases the crack length and decreases the crack shape ratio. The fatigue life of MFCs with different crack sizes is mostly determined by the larger crack, while the shape ratio effect can be ignored. The crack number has the most significant effect on fatigue life, where the five-cracks case reducing the fatigue life by up to 39 % compared to a single crack. Due to the nonlinear effect of crack spacing on fatigue life, MFCs with the same fatigue life may have significantly different final crack length. Therefore, the proposed failure criterion for MFCs considers the effects of both crack depth and length.

Development and safety evaluation of an adaptive personalized speed guidance system for on-ramp merging in highway service areas

As autonomous driving and Vehicle-to-Everything (V2X) technologies evolve, the efficiency and safety of ramp merging in the highway service areas have become increasing critical. This study introduces a closed-loop feedback speed guidance system that accommodates individual driving styles, aiming to optimize merging behaviour, reduce traffic accidents, and enhance total traffic efficiency. The system dynamically adjusts the merging vehicle speeds by continuously monitoring their speed and location with variable steps to promote smoother merging. Moreover, this research also involves collecting naturalistic driving data from ramp merging scenarios, using the K-means clustering and point estimation method to recognize and analyse driving style characteristics, and integrating these styles into the developed closed-loop feedback speed guidance system. This approach results in personalized speed guidance curves tailored to different driving styles, facilitating more efficient mering. Additionally, the study conducts a Safety of the Intended Functionality (SOTIF) evaluation of this system using the System-Theoretic Process Analysis (STPA) method, which helps identify potential security risks and develop appropriate mitigation strategies to ensure the system’s safe and stable operation. The simulation results confirm that this innovative dynamic speed guidance system substantially improves traffic safety and efficiency in ramp merging areas.

Analysis of mixed traffic flow characteristics based on cellular automata model under lane management measures

In the future, mixed traffic flow comprising Human-Driven Vehicles (HDVs), Connected and Autonomous Vehicles (CAVs), and CAV platoons will coexist for an extended period. Exploring the operational characteristics of mixed traffic flow under different lane management measures is essential for effective management and control. Initially, we analyze the motion characteristics of HDVs, CAVs, and CAV platoons and identify the car-following types within mixed traffic. Based on vehicle motion characteristics and the variance in maximum desired speeds among HDV drivers, we establish longitudinal motion rules for HDVs using a safety distance model. For CAVs and their platoons, we develop longitudinal motion rules by considering platoon merging and splitting behaviors, as well as speed and spacing error requirements for platoon driving. Subsequently, we formulate lateral lane-change rules based on the Symmetric Two-lane Cellular Automata (STCA) model, considering differences in reaction times and following distances between HDVs and CAVs. Finally, we conducted simulation experiments on a unidirectional three-lane highway using the multi-lane mixed traffic flow cellular automata model, analyzing the characteristics of mixed traffic under various lane management measures, such as mixed lane, CAV-dedicated lane, and CAV-priority lane. The results show that as the proportion of CAV (denoted as p) increases, the traffic flow capacity, optimal density, and jam density also increase. When p≤0.6, the k−q diagram exhibits a triangular shape, whereas for p>0.6, it assumes a trapezoidal shape. Implementing CAV-dedicated lane can reduce Cooperative Adaptive Cruise Control (CACC) degradation rates but only enhances traffic flow capacity when the CAV proportion reaches a certain threshold. Compared to the mixed lane scheme, when p≤0.3 and k≤0.3, the CAV-priority lane schemes not only meet traffic demands but also reduce CACC degradation rates. The vehicle speed in CAV-priority lane surpasses that in HDV lanes, facilitating improved traffic efficiency for CAVs. The distribution of maximum speeds among HDV drivers affects the fundamental diagram of mixed traffic flow and the performance of CAV-priority lane, with greater impacts observed as the standard deviation of the HDVs' maximum speed increases.

New insights into the merging behavior of pedestrians: Quantification of lane competition level

Merging behavior is a complex crowd movement pattern caused by certain architectural features and has triggered serious crowd accidents worldwide. It is not uncommon to find such merging structures on the floor plans of some underground infrastructures, which deserve more detailed studies on special crowd movement patterns to enhance the safety level. Researchers have demonstrated that the unstable emotions and competitive behaviors of pedestrians during underground evacuation need to be taken into account due to the enclosed environment. However, there are no relevant studies focused on the competition characteristics among pedestrians during the merging process in a quantitative way. The comprehensiveness and systematization of the geometric features of typical merging structures, as well as the way to create the perception of competition and conflict among pedestrians during real emergencies also need to be improved. In this paper, we carried out a series of controlled experiments in symmetric and asymmetric merging structures with different channel widths and incentive levels to investigate crowd merging behavior with competition characteristics. The density and delay time within the merging area, as well as the time interval and lane changes before and after merging are analyzed. By introducing and modifying Simpson’s Diversity Index, we proposed a new method for quantifying the competition level among pedestrians during the merging process. It is concluded that the performance of the merging area is sensitive to the channel widths and the merging layouts, and the negative effect of unbalanced walking priority is more significant under high incentive level. The lane competition level during the merging process is correlated with the effects of various geometric features, movement layouts, and incentive levels. The method proposed in this paper brings new insights into the interpretation of merging scenarios and more studies on pedestrian dynamics. We hope that this study could serve as supplementary work for other modeling or field experiment methods to jointly provide more insights into the crowd management strategies in some underground infrastructures.

A Microscopic On-Ramp Model Based on Macroscopic Network Flows

While macroscopic traffic flow models adopt a fluid dynamic description of traffic, microscopic traffic flow models describe the dynamics of individual vehicles. Capturing macroscopic traffic phenomena accurately remains a challenge for microscopic models, especially in complex road sections. Based on a macroscopic network flow model calibrated to real traffic data and new rules for the acceleration and merging behavior on the on-ramp, we propose a microscopic model for on-ramps. To evaluate the performance of the new flow-based model, we conduct traffic simulations assessing speeds, accelerations, lane change positions, and risky behavior. Our results show that, although the proposed model exhibits some limitations, its performance is superior to the Intelligent Driver Model in the evaluated aspects. While the Intelligent Driver Model simulations are almost free of conflicts, the proposed model evokes a realistic amount and severity of conflicts and therefore can be considered for safety analysis.

Role of Freeway Ramp Geometry on Driver Acceleration and Merging Behavior

Role of Freeway Ramp Geometry on Driver Acceleration and Merging Behavior

Extracting Vehicle Trajectories from Partially Overlapping Roadside Radar.

This work presents a methodology for extracting vehicle trajectories from six partially-overlapping roadside radars through a signalized corridor. The methodology incorporates radar calibration, transformation to the Frenet space, Kalman filtering, short-term prediction, lane-classification, trajectory association, and a covariance intersection-based approach to track fusion. The resulting dataset contains 79,000 fused radar trajectories over a 26-h period, capturing diverse driving scenarios including signalized intersections, merging behavior, and a wide range of speeds. Compared to popular trajectory datasets such as NGSIM and highD, this dataset offers extended temporal coverage, a large number of vehicles, and varied driving conditions. The filtered leader-follower pairs from the dataset provide a substantial number of trajectories suitable for car-following model calibration. The framework and dataset presented in this work has the potential to be leveraged broadly in the study of advanced traffic management systems, autonomous vehicle decision-making, and traffic research.

Freeway merging trajectory prediction for automated vehicles using naturalistic driving data

Due to differences in speed and the complex interaction between merging and through-lane vehicles at freeway merging sections, crashes involving both human drivers and automated vehicles (AVs) persist. To assist AVs to predict the intentions of surrounding vehicles for further dynamic motion planning, researchers have focused on developing trajectory prediction algorithms. Few studies, however, have developed merging trajectory prediction models using naturalistic driving data in China, making it urgent to put it on the agenda for AVs’ safety and efficiency at freeway merging sections. Based on merging periods extracted from the Shanghai Naturalistic Driving Study (SH-NDS), this study compares merging behavior on freeways with through-lane speed limits of 80 km/h, 100 km/h, and 120 km/h using analysis of variance (ANOVA). Merging trajectory prediction algorithms for these three speed limit cases are trained and tested using backpropagation neural network (BPNN) and long short-term memory neural network (LSTM NN) approaches. Results show 1) significant differences among the three cases in all merging behavior variables except for longitudinal gap; and that 2) the BPNN algorithm for merging trajectory prediction demonstrates superior performance compared to the LSTM NN. Two major contributions to the safe operation of AVs are provided: 1) the developed algorithms can be integrated into AV systems to accurately predict real-time desired trajectories of nearby merging vehicles in uncongested traffic conditions, and assist ongoing motion planning strategies for AVs; and 2) the algorithms can be incorporated in simulation tests for AV safety evaluation involving freeway merging sections.

Modelling Merging Behaviour of Drivers in Heterogeneous Traffic at Roundabouts

Roundabouts are among the widely used traffic control infrastructures, which function by defining priorities for various entry approaches and circulating traffic. In many developing countries, the priority rules are not implemented through pavement markings or road signs and drivers are also unaware of these rules. Thus, available gaps become the only criteria for merging and movement of vehicles through the roundabout. It has been observed that the gap acceptance behaviour of drivers also depends on the type of vehicle in heterogeneous traffic streams. The main objective of this study is to investigate the difference in gap acceptance behaviour of motorcycle riders and car drivers at roundabouts. The gaps accepted by car drivers and motorcycle riders under different traffic conditions are measured from the recorded video of the traffic stream at a selected roundabout using an unmanned aerial vehicle. The critical gap is estimated using Raff’s method and its relationship with circulatory volume and approach speed is analysed. The results show that the critical gap for motorcycles (2.46 seconds) is 23.3% smaller than the critical gap for cars (3.21 seconds). Motorcycles can move through smaller gaps in comparison with cars due to their size and ease of manoeuvring. Results also show that motorcycles tend to merge with greater approach speed as the available gap within the circulating vehicles reduces, whereas the cars are observed following an opposite trend.

Effects of confined distance and wire spacing on flame spread over double electrical wires for the layout design in buildings

In this study, the flame merging and flame spread behaviors over two identical parallel electrical wires (the ratios of copper core diameter to entire wire diameter are: 6mm/10 mm and 8mm/12 mm for typeⅠand typeⅡ, respectively) were experimentally investigated under different wire spacings (3∼18 mm) and confined distances(10∼30 mm). The results show that the smaller confined distance D will promote the merging, making the wire spacing s increase for the fully merging. With the increase of D, the flame width and flame spread rate Vf will increase.While the flame height will be stretched at smaller D. And Vf is larger than that of single wire at the same D for different wire spacings.The interesting finding is that a pool fire is formed at the middle of the wire spacing when s and D are small, which will enhance the flame spread.The heat flux feedback of components including of the conductive heat flux q˙c″, the first single flame heat flux q˙f1″, the second flame heat flux q˙f2″, the pool fire heat flux q˙pf″ and the gypsum board heat flux q˙g″ are quantitatively analyzed. At the intermittent and non merging stages, the convective and radiative heat fluxes q˙vf2″ and q˙rf2″ will decrease largely due to the interaction weakened greatly. Based on the heat transfer analysis, a flame spread model is established, which can well predict the flame spread rate.These findings can give the useful suggestions on the layout design of electrical wires in confined space in buildings.

A Stackelberg game-based on-ramp merging controller for connected automated vehicles in mixed traffic flow

ABSTRACT This paper proposes a game theory-based on-ramp merging controller for connected automated vehicles (CAVs) in mixed traffic flow. First, a two-layer decision-making framework based on the Stackelberg game is designed to consider the fuel consumption and safety payoffs of mixed traffic flow under different driving behaviors. The upper layer of the framework determines the optimal merging decision (i.e. merging time and location) for on-ramp vehicles (RVs) based on the Stackelberg game. The lower layer optimizes the merging trajectory of CAVs to reduce energy consumption and safety risks during the ramp-merging process. Then, a driving behavior estimation algorithm is developed to describe the differences in mainline vehicles (MLVs) response to the merging behavior of RVs. Finally, the simulation experiments are adopted to verify the effectiveness and stability of the proposed framework. The results indicated that, the proposed framework promotes environmental protection, operational efficiency, and traffic flow stability in different traffic scenarios.

Experimental study on the flame merging and temperature profile produced by two pool fires beneath the curved ceiling

Compared to single-source fires in tunnels, double-source fires may exhibit more intricate flame merging behavior and higher flame burning rates due to their interaction, thereby escalating the risk of fire propagation within the tunnel. In this investigation, a 1:10 scale model of a horseshoe-shaped tunnel test site was deployed to conduct experiments on lateral double-source fires, scrutinizing flame merging behavior, probability prediction models, and temperature characteristics of tunnel ceiling smoke gas by varying the oil pool size and double-source spacing. Findings from the study suggest that flame merging behavior within the tunnel is contingent upon double-source spacing and heat release rate (HRR), manifesting in three distinct forms: complete merging, intermittent merging, and complete separation. Through the introduction of non-dimensional heat release rate and non-dimensional fire source spacing, a piecewise function has been devised to forecast the probability of flame merging for both fire types. Furthermore, a predictive model for the maximum temperature rise along the longitudinal centerline of a horseshoe tunnel was formulated, drawing from established theoretical frameworks.

Experimental study on upward fire spread of polyurethane foam under double fire source conditions with different restricted facade Structures

This paper examines the impact of insulation material (PU) fire spread characteristics under dual fire source conditions considering various restricted facade Structures. It is found that the merging tendency is affected by the flame inclination angle θ1 and mapping length L. Additionally, the flame merging behavior is determined by the combined effects of the chimney effect, inter-flame air entrainment, and heat transfer changes caused by the restricted angle. The Inter-flame air entrainment λ was introduced to confirm that there is a positive correlation between flame height and Inter-flame air entrainment in the large-angle restricted facade Structures (θ=120°,150°，180°). By introducing the hydraulic diameter Deff* to compare the experimental data with the previous flame pulsation frequency model, the proposed f=0.5g/Deff* model can further improve the accuracy of the prediction results. During the flame merging stage, the axial temperature field distribution on the PU board surface was compared. The study found that the restricted facade Structures resulting in a cooling effect due to fresh air entrainment at the bottom with the chimney effect. As a result, the small-angle restricted facade Structures (θ=30°，60°，90°) led to an upper-high and lower-low distribution of axial temperature.

Unraveling Spatial–Temporal Patterns and Heterogeneity of On-Ramp Vehicle Merging Behavior: Evidence from the exiD Dataset

Understanding the spatiotemporal characteristics of merging behavior is crucial for the advancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merging patterns, and investigate how various factors, such as merging scenarios and vehicle types, influence driving behavior. Initially, a framework based on a high-definition (HD) map is developed to extract trajectory information in a meticulous manner. Subsequently, eight distinct merging patterns are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane’s spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifies high-risk situations. Utilizing the latest aerial dataset, exiD, which provides HD map data, the study presents novel findings. Specifically, it uncovers patterns where the following vehicle in the target lane chooses to accelerate and overtake rather than cutting in front of the merging vehicle, resulting in Time-to-Collision (TTC) values of less than 2.5 s, indicating a significantly higher risk. Moreover, the study finds that differences in merging speed, distance, and duration can be disregarded in patterns where vehicles are present both ahead and behind, or solely ahead, suggesting these patterns could be integrated for simulation to streamline analysis and model development. Additionally, the practice of truck platooning has a significant impact on vehicle merging behavior. Overall, this study enhances the understanding of merging behavior, facilitating autonomous vehicles’ ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic management, and enabling the effective integration of autonomous driving systems with human drivers.

Coalescence of liquid marbles on oil-infused surface

While droplets typically merge instantly in an air medium, alterations to the outer medium can complicate the coalescence process. This study investigates droplet coalescence dynamics when encapsulated by a uniform liquid–solid composite shell, aiming to understand its implications for mechanical stability and merging behavior. The uniform shell around a sessile droplet is produced by using liquid marble on oil-infused surfaces (LMOI). The coalescence dynamics was studied under two different conditions: droplet with LMOI and LMOI with LMOI. In contrast to merging of bare droplets, coalescence involving at least one LMOI reveals a three-step process, including spreading, depletion, and eventual merging phases. Higher oil viscosity influences the merging process, with increased viscosity leading to delayed merging with longer spreading and depletion phases. LMOI exhibits significant resistance to merging with another LMOI, necessitating external triggers like pressure or electric fields for coalescence. These findings provide insights into designing microreactor systems based on LMOI, contributing to the comprehension of their dynamics and functionalities.

Bubble Unidirectional Transportation on Multipath Aerophilic Surfaces by Adjusting the Surface Microstructure.

Comprehending and controlling the behavior of bubbles on solid surfaces is of significant importance in various fields including catalysis and drag reduction, both industrially and scientifically. Herein, Inspired by the superaerophilic properties of the lotus leaf surface, a series of asymmetrically patterned aerophilic surfaces were prepared by utilizing a facile mask-spraying method for directional transport of underwater bubbles. The ability of bubbles to undergo self-driven transportation in an asymmetric pattern is attributed to the natural tendency of bubbles to move toward regions with lower surface energy. In this work, the microstructure of the aerophilic surface is demonstrated as a critical element that influences the self-driven transport of bubbles toward regions of lower surface energy. The microstructure characteristic affects the energy barrier of forming a continuous gas film on the final regions. We classify three distinct bubble behaviors on the aerophilic surface, which align with three different underwater gas film evolution states: Model I, Model II, and Model III. Furthermore, utilizing the energy difference between the energy barrier that forms a continuous gas film and the gas-gas merging, gas-liquid microreaction in a specific destination on the multiple paths can be easily realized by preinjecting a bubble in the final region. This work provides a new view of the microevolutionary process for the diffusion, transport, and merging behavior of bubbles upon contact with an aerophilic pattern surface.

Changing Regularity of the Interaction Effects of Multi-Scale Factors on Drivers’ Merging Behaviors in the Highway Work Zone

This study aimed to explore the impacting mechanism of macro- and micro-factors and multi-scale synergy on drivers’ merging behaviors in the highway work zone, and then to facilitate future merging prediction research. The merging behavior, requiring drivers to detect adjacent vehicles and real-time traffic conditions simultaneously, is a complex cognitive process. Previous studies have mainly focused on the stable impacts of limited factors on merging probability, but ignored the varying states and drivers’ performance. Status-changing positions in the whole merging process were first identified by constructing and analyzing the relationship between running speed and distance from the construction area. Subsequently, the interaction analysis was conducted among the multi-scale traffic factors, utilizing optimized logistic models and interaction estimates, thus establishing macro- and micro-factor connections. Besides, the marginal effect was calculated to analyze the fluctuation degree of these connections. Finally, a multilevel identification framework was proposed, whose effectiveness and practicality were validated using 744 naturalistic vehicular trajectories from a real highway work area. At different positions, drivers are affected by various factors to varying degrees. While approaching the construction area, drivers become more passive, thus trying to avoid rear-ending the lead vehicle but ignoring the lag vehicle. Besides, traffic volume also affects drivers’ merging decisions by confusing their cognition toward the time headway. This research indicates that dynamic interaction effects between multi-scale factors could provide far-reaching benefits for lane-changing prediction. The findings provide a basis for formulating traffic management policies and constructing driving assistance systems.

Towards Safer Highway Work Zones: Insights from Deep Learning Analysis of Thermal Footage

Thermal imaging, when coupled with deep-learning-based analysis, can significantly improve highway work zone management and safety, though data scarcity in this field has been a historical challenge. We collected over 440 hours of thermal footage from highway sites in Massachusetts and New Hampshire and created a vehicle segmentation dataset featuring over 14,000 vehicle instances. We implement effective training strategies for deep learning models and develop algorithms to analyze vehicle trajectories and merging behaviors. Various visualization techniques are illustrated to better understand the extensive analytics data generated for each site, which can lead to development of data-driven strategies to improve Traffic management and safety. GitHub repository: https://github.com/z00bean/SmartWorkZoneControl.

Bubble flow analysis using multi-phase field method

In simulations of gas-liquid two-phase flows using conventional interface capture methods, we observed that when bubbles come close to each other, they tend to merge numerically, despite experimental evidence indicating that they would repel each other. Given the significant impact of sequential numerical coalescence on flow patterns, it is necessary to regulate the merging behavior of close bubbles. To address this issue, we introduced the Multi-Phase Field (MPF) method, which mitigates bubble coalescence by applying an independent fluid fraction function to each bubble. In this study, we employed the MPF based on the N-phase model [7] to minimize numerical errors associated with surface interactions at triple junction points. Additionally, we implemented the Ordered Active Parameter Tracking (OAPT) method [9] to efficiently store several hundreds of fluid fraction functions. To validate the MPF method, we conducted analysis of turbulent bubbly pipe flows and compared the results against experimental data from Colin et al [12]. The validation results showed reasonable agreements with respect to the bubble distribution and the flow velocity profiles.

Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?

Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?