Search Results (17)

Legal requirements for minimum distances between vehicles are often not met for short periods of time, especially when changing lanes on multi-lane roads. These situations are typically non-hazardous, as human drivers anticipate surrounding traffic, allowing for shorter headways and improved traffic flow. Automated vehicles (AVs), however, are typically designed to maintain strict headway limits, potentially reducing traffic efficiency. Therefore, legal questions arise as to whether mandatory gap and headway limits for AVs may be violated during periods of non-compliance. While traffic flow simulation is a common method for analyzing AV impacts, previous studies have typically modeled AV behavior using driver models originally designed to replicate human driving. These models are not well suited for representing clearly defined, structured non-compliant maneuvers, as they cannot simulate intentional, rule-deviating strategies. This paper addresses this gap by introducing a concept for AV non-compliant behavior and implementing it as a module within a pre-existing AV driver model. Simulations were conducted on a three-lane highway with an on-ramp under varying traffic volumes and AV penetration rates. The results showed that, with an AV-penetration rate of more than 25%, road capacity at highway entrances could be increased and travel times reduced by over 20%, provided that AVs were allowed to merge with a legal gap of 0.9 s and a minimum non-compliant gap of 0.6 s lasting up to 3 s. This suggests that performance gains are achievable under adjusted legal requirements. In addition, the proposed framework can serve as a foundation for further development of AV driver models aiming at improving traffic efficiency while maintaining regulatory compliance. Full article

As autonomous vehicles (AVs) are gradually integrated into existing traffic systems, understanding their impact on freeway operations becomes essential for effective infrastructure planning and policy design. This study explores how AV penetration rates, behavior profiles, and freeway geometry interact to influence traffic performance and safety. Using microscopic simulations in VISSIM (a high-fidelity traffic simulation tool), four typical freeway segment types—basic sections, weaving zones, on-ramp merging areas, and AV-exclusive lanes—were modeled under diverse traffic demands and AV behavior settings. The findings indicate that, while AVs can improve flow stability in simple environments, their performance may deteriorate in complex merging scenarios without supportive design or behavior coordination. AV-exclusive lanes offer some mitigation when AV share is high. These results underscore that AV integration requires context-specific strategies and cannot be universally applied. Adaptive, behavior-aware traffic management is recommended to support a smooth transition toward mixed autonomy. Full article

With the rapid advancement of urbanization, the problem of traffic congestion in cities has become increasingly severe. Effectively managing traffic congestion is crucial for sustainable urban development. Previous studies have indicated that fluid dynamics theory can be applied to address flow problems in transportation, and this article aims to utilize CFD to solve congestion issues in urban road traffic. Firstly, a similarity analysis is conducted between fluids and traffic flow at the theoretical level. By converting parameters, the formula of fluid is derived into the formula of traffic flow, thus demonstrating the feasibility of using CFD in traffic flow research. On this basis, targeting recurrent congestion and non-recurrent congestion scenarios, 2D road fluid domains and constraints are constructed based on the common characteristics of each congestion type area. By using Fluent (2018) software to analyze the flow conditions under different congestion characteristics, the smoothness of fluid motion can be used to find out the problems causing traffic congestion and conduct an analysis to reveal the microscopic mechanism behind congestion formation. For different types of congestion, in order to clarify the effectiveness of congestion mitigation measures, the geometric design of road intersections and diversion measures are discussed in depth. The traffic pressure is analyzed by adjusting the vehicle inlet angle at intersections or controlling the vehicle flow speed. Finally, the optimal design scheme is obtained by comparative analysis. It is concluded that for the roundabout, it is optimal to change the entrance angle to 20°. For the on-ramp merging area, it is optimal to set the ramp entrance as a parallel ramp. For recurrent congestion, it is required to pass at an optimal speed of 30 km/h. Based on the theory of previous studies, this paper further proves that the congestion degree of traffic flow under specific assumptions can be expressed by the pressure of the fluid. It also provides new ideas for optimizing urban road design and solving vehicle traffic congestion problems. Full article

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. Full article

Accurate identification and analysis of traffic conflicts through surrogate safety measures (SSMs) are crucial for safety evaluation in road systems. Existing SSMs for conflict identification and analysis mostly consider the temporal–spatial proximity of conflicts without taking into account the severity of potential collisions. This makes SSMs unsuitable for traffic safety evaluation in complex road environments. In order to address the shortcomings above, this study first introduces a new SSM called the Potential Conflict Risk Index (PCRI). To validate the effectiveness of PCRI, the inD dataset is adopted for conflict identification comparison between time-to-collision (TTC) and PCRI. Using PCRI, this study conducts a conflict analysis in the freeway merging areas based on the data from the Outer Ring Expressway Dataset (ORED), accounting for differences between cars and trucks. The comparative results between TTC and PCRI show that PCRI can provide a more comprehensive identification of conflicts and a more accurate identification of the moment with the highest conflict risk. The results of conflict analysis suggest that conflicts occur more frequently in situations involving trucks, and these conflicts commonly occur in closer proximity to the on-ramp at freeway merging areas. The findings from this study can improve the accuracy of conflict identification under different conflict patterns, enhancing the specificity of traffic safety measures and ultimately ensuring the safety of road systems. Full article

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. Full article

A ramp merging decision as an important part of the lane change model plays a crucial role in the efficiency and safety of the entire merging process. However, due to the inevitability of on-ramp merging, the limitations of the road environment, and the conflict between the merging vehicle and the following vehicle on the main road, it is difficult for human drivers to make optimal decisions in complex merging scenarios. First, based on the NGSIM dataset, a gain function is designed to represent the interaction between the ego vehicle (EV) and the surrounding vehicles, and the gain value is then used as one of the characteristic parameters. The K-means algorithm is employed to conduct a cluster analysis of the driving style under the condition of changing lanes. This paper models the interaction and conflict between the ego vehicle (vehicle merging) and the mainline lagging vehicle as a complete information non-cooperative game process. Further, various driving styles are coupled in the ramp decision model to mimic the different safety and travel efficiency preferences of human drivers. After EV decision-making, a quintic polynomial method with multi-constraints is proposed to implement merging trajectory planning. The proposed algorithm is tested and analyzed in an on-ramp scenario, and the results demonstrate that drivers with different driving styles can make correct decisions and complete the ramp merging. The changing trend of the speed and trajectory tests are also in line with the features of the driver’s driving style, offering a theoretical foundation for individualized on-ramp merging decisions. Full article

Traffic breakdown is the transition of traffic flow from an uncongested state to a congested state. During peak hours, when a large number of on-ramp vehicles merge with mainline traffic, it can cause a significant drop in speed and subsequently lead to traffic breakdown. Therefore, ramp meters have been used to regulate the traffic flow from the ramps to maintain stable traffic flow on the mainline. However, existing traffic breakdown prediction models do not consider on-ramp traffic flow. In this paper, an algorithm based on artificial neural networks (ANN) is developed to predict the probability of a traffic breakdown occurrence on freeway segments with merging traffic, considering temporal and spatial correlations of the traffic conditions from the location of interest, the ramp, and the upstream and downstream segments. The feature selection analysis reveals that the traffic condition of the ramps has a significant impact on the occurrence of traffic breakdown on the mainline. Therefore, the traffic flow characteristics of the on-ramp, along with other significant features, are used to build the ANN model. The proposed ANN algorithm can predict the occurrence of traffic breakdowns on freeway segments with merging traffic with an accuracy of 96%. Furthermore, the model has been deployed at a different location, which yields a predictive accuracy of 97%. In traffic operations, the high probability of the occurrence of a traffic breakdown can be used as a trigger for the ramp meters. Full article

To solve the problems of congestion and accident risk when multiple vehicles merge into the merging area of a freeway, a platoon split collaborative merging (PSCM) method was proposed for an on-ramp connected and automated vehicle (CAV) platoon under a mixed traffic environment composed of human-driving vehicles (HDV) and CAVs. The PSCM method mainly includes two parts: merging vehicle motion control and merging effect evaluation. Firstly, the collision avoidance constraints of merging vehicles were analyzed, and on this basis, a following–merging motion rule was proposed. Then, considering the feasibility of and constraints on the stability of traffic flow during merging, a performance measurement function with safety and merging efficiency as optimization objectives was established to screen for the optimal splitting strategy. Simulation experiments under traffic demand of 1500 pcu/h/lane and CAV ratios of 30%, 50%, and 70% were conducted respectively. It was shown that under the 50% CAV ratio, the average travel time of the on-ramp CAV platoon was reduced by 50.7% under the optimal platoon split strategy compared with the no-split control strategy. In addition, the average travel time of main road vehicles was reduced by 27.9%. Thus, the proposed PSCM method is suitable for the merging control of on-ramp CAV platoons under the condition of heavy main road traffic demand. Full article

In highway on-ramp sections, the conflictual interactions between a subject vehicle (merging vehicle) in the acceleration lane and a following vehicle (lagging vehicle) in the adjacent mainline can lead to traffic congestion, go–stop oscillations, and serious safety hazards. Human drivers combine their previous lane-changing experience and their perception of surrounding traffic conditions to decide whether to merge. However, the decisions that they make are not always optimal in specific traffic scenarios due to fuzzy perception and misjudgment. That is, they make lane-changing decisions in a bounded rational way. In this paper, a game-theory-based approach is used to model the interactive behavior of mandatory lane-changing in a highway on-ramp section. The model comprehensively considers vehicle interactions and the bounded rationality of drivers by modeling lane-changing behavior on on-ramps as a two-person non-zero-sum non-cooperative game with incomplete information. In addition, the Logit QRE is used to explain the bounded rationality of drivers. In order to estimate the parameters, a bi-level programming framework is built. Vehicle trajectory data from NGSIM and an unmanned aerial vehicle survey were used for model calibration and validation. The validation results were rigorously evaluated by using various performance indicators, such as the mean absolute error, root mean square error, detection rate, and false-alarm rate. It can be seen that the proposed game theory-based model was able to effectively predict merging and yielding interactions with a high degree of accuracy. Full article

Highway merging points are critical elements due to the interactions between merging vehicles and following vehicles on the outermost lane of the highway stream. Such interactions could have significant implications for safety and capacity at ramp locations. The aim of this study was to investigate the spacing adjustment behavior by the interacting drivers at merging locations. In this regard, we relied on the NGSIM trajectory dataset to investigate the impacts of the speed difference between the following and merging vehicles on a space headway, considering different geometric designs and vehicle classes. Nonlinear regression models were estimated to analyze the interactions. The results showed a significant and exponential tendency for headway reduction, particularly when the difference in speed was higher than 30 km/h. In addition, the findings revealed that the highway with an auxiliary lane performed better in terms of headway reduction. Furthermore, the space headway reduction trend was higher when the following vehicle was a truck rather than a car. Policymakers and practitioners aiming to improve road safety at merging locations could use this study’s findings. The resulting parameters can also be utilized in microsimulation models, e.g., for headway adjustment behavior in car-following models. Full article

On-ramp merging areas are essential parts of freeways. The merging behavior of vehicles is the main factor affecting the continuity of freeway traffic flow, which determines the capacity of the main freeway line. With the development of innovative car technology, ACC technology has been widely used in actual vehicles. At the same time, the public’s demand for freeway-speed improvement is increasing. However, the evaluative research on freeway-speed-improvement schemes, safety, and efficiency, is incomplete. Therefore, this paper aims at the study of the mixed traffic flow of ACC and human-driven vehicles, simultaneously increasing the maximum speed limit to 140 km/h, and establishes a ramp-entry model through the SUMO simulation platform. The traffic-flow parameters upstream of the ramp entry and downstream of the weaving area are analyzed, including the flow, average speed, headway, and lane-change rate. The influence of the driving conditions for mixed ACC vehicles with different proportions in the ramp-merging scenario is analyzed from efficiency and safety perspectives. Studies have shown that mixing ACC vehicles can improve the safety and efficiency of the road, and the increase in the maximum speed limit has limited road capacity. When considering the inclusion of ACC vehicles, increasing the maximum speed limit can improve the operating efficiency of the road. Full article

The highway on-ramp merging area is one of the major sections that form traffic bottlenecks. In a connected vehicle environment, V2V and V2I technologies enable real-time exchange of information, including position, speed, and acceleration. To improve the efficiency of vehicle merging at the on-ramp, this study proposes a cooperative merging control strategy for network-connected autonomous vehicles. First, the central controller designs the merging sequence and safety space for vehicles passing through the confluence point. Then, a trajectory optimization model was constructed based on vehicle longitudinal dynamics, and the PMP algorithm was used to determine the optimal control input. Finally, all vehicles follow the optimal trajectory so that the ramp vehicles merge smoothly into the mainline. Simulations verify that the proposed algorithm performs better than FIFO, with 13.2% energy savings, 41.4% increase in average speed, and 50.4% reduction in travel time over the uncontrolled merging scenario. The method is further applied to different traffic flow conditions and the results show that it can significantly improve traffic safety and mobility, while effectively reducing vehicle energy consumption. However, the traffic operation improvement is not satisfactory under low traffic demand. Full article

Connected and automated vehicles (CAVs) are a very promising alternative for reducing fuel consumption and improving traffic efficiency when vehicles merge at on-ramps. In this study, we propose a graph-based method to coordinate CAVs to merge at the highway ramp. First, the optimized vehicles were divided into groups to pass the merging point. Then we built a directed graph model for each group of vehicles, where each path of the graph corresponds to one of all possible merging sequences. The improved shortest path algorithm is proposed to find the optimal merging sequence for minimizing total fuel consumption. The results of the simulation showed that the proposed graph-based method reduced fuel consumption and ensured high traffic efficiency; moreover, the vehicles can form a platoon after passing the merge point. Full article

The merging area of the freeway is an area with a high incidence of traffic accidents. With the development of connected and automated vehicles (CAVs) and V2X technology, the traffic efficiency of freeway ramp areas has been significantly improved. However, current research mostly focuses on merging a single mainline lane and ramp, and there are few cases of multiple lanes. In this paper, we present a collaborative merging model with a rule-based lane-changing strategy in a V2X environment. First, the vehicle selects the appropriate gap to change lanes safely without affecting other vehicles. Meanwhile, we established a linear time discrete model to optimize the trajectory of vehicles in real-time. Finally, the proposed model and strategy were implemented in SUMO and Python. The simulation results showed that the merging model we proposed based on the lane-changing strategy had good performance in terms of the number of stops, average delay, and average speed. Full article