Search Results (32)

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

In recent years, congestion on port motorways has become increasingly frequent, significantly constraining transportation efficiency and contributing to higher pollution emissions. This paper proposes a novel max-pressure-driven integrated control (IFC-MP) for port motorways, inspired by the max pressure (MP) concept, which continuously adjusts the weights of ramp metering (RM) and the variable speed limit (VSL) based on pressure feedback from the on-ramp and upstream, assigning greater control weight to the side with higher pressure. A queue management mechanism is incorporated to prevent on-ramp overflow. The effectiveness of IFC-MP is verified in SUMO, filling the gap where the previous integrated control methods for port motorways lacked micro-simulation validation. The results show that IFC-MP enhances bottleneck throughput by approximately 7% compared to the no-control case, optimizes the total time spent (TTS) by 26–27%, and improves total pollutant emissions (TPEs) by about 11%. Compared to strategies that use only RM and VSL control, or activate VSL control only after RM reaches its lower bound, the time–space distribution of speed under IFC-MP is more uniform, with smaller fluctuations in bottleneck occupancy. Additionally, IFC-MP maintains relatively stable performance under varying compliance levels. Overall, the IFC-MP is an effective method for alleviating congestion on port motorways, excelling in optimizing both traffic efficiency and pollutant emissions. 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

The traditional non-cooperative and cooperative game methods have limitations in solving the traffic problems of autonomous or assisted driving vehicles using vehicle-to-everything communication. In this paper, the biform game method is introduced to optimize the lane-changing behavior of autonomous or assisted driving vehicles in highway on-ramp areas based on vehicle-to-everything. Considering the lane-changing and speed adjustment needs of autonomous vehicles in high-speed scenarios, a forced lane-changing framework was constructed, and the speed gain allocation was determined based on the target vehicle lane-changing time, and a speed increase was regarded as a benefit. Through the constructed biform game model, research was carried out on conflicting and cooperative vehicles. A strategy combination is first constructed in the non-cooperative situation, and then the cooperative game competition stage begins. The Shapley value is used to deduce the distribution value of each participant in the cooperative game stage, which is the profit value in the non-cooperative stage, and then the pure-strategy Nash equilibrium solution is calculated. The interaction with other vehicles in the lane-change process is based on maximizing the benefit to all the vehicles participating in the lane change, and the optimal speed solution of the biform game model when changing lanes is obtained. Numerical examples were used to verify the validity and feasibility of the model and broaden the application range of the biform game method. In future research, this method will be applied to more complex traffic models, such as driving models in emergency situations and research from the perspective of road infrastructure designers, providing new ideas and directions for optimization strategies for autonomous vehicle lane changes in the Internet of Vehicles. Full article

In the connected and autonomous vehicle (CAV) environment, vehicles with different levels of automation are being deployed on public roads. Most research focuses on traffic flow simulation for a single vehicle type, while there are few studies on the interactions of mixed traffic involving CAVs, autonomous vehicles (AVs), and human-driven vehicles (HDVs). To fill this gap, this study investigates the traffic performance of heterogeneous traffic on multi-lane ring roads and highways with on-ramps. Leveraging the Python and SUMO simulation platform, the JAD strategy is introduced to optimize the dynamic interactions within heterogeneous traffic flow. Various scenarios with different proportions of CAVs, AVs, and HDVs were simulated to assess their impact on traffic efficiency, dynamics, safety, and environmental factors. The findings indicate that traffic efficiency, stability, and environmental impact improve as the share of HDVs declines and the proportion of CAVs and AVs rises. In scenarios with more HDVs, the improvements are minimal. Traffic safety gradually improves as the proportion of CAVs and AVs increases, with significant improvements observed when CAVs account for 40% of vehicles on ring roads and 50% on highways. This study advances the understanding of complex interactions in mixed traffic scenarios and their implications for traffic management. Full article

To improve the detection accuracy of the drone-based oriented vehicle object detection network and establish high-accuracy vehicle trajectory datasets, we present a freeway on-ramp vehicle (FRVehicle) detection dataset with oriented bounding box annotations for vehicles in freeway on-ramp scenes from drone videos. Based on this dataset, we analyzed the dimension and angle distribution patterns of road vehicle object oriented bounding boxes and designed an Asymmetric Selective Kernel Network. This algorithm dynamically adjusts the receptive field of the backbone network’s feature extraction to accommodate the detection requirements for vehicles of different sizes. Additionally, we estimate vehicle heights with high-precision object detection results, further enhancing the accuracy of the vehicle trajectory. Comparative experimental results demonstrate that the proposed Asymmetric Selective Kernel Network achieved varying degrees of improvement in detection accuracy on both the FRVehicle dataset and DroneVehicle dataset compared to the symmetric selective kernel network in most scenarios, validating the effectiveness of the method. 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

Linear spatial data are primarily used in Geographic Information Systems (GISs) to represent spatial data in the form of roads, rivers, railways, and utility lines. Linear spatial data are mostly composed of one-dimensional linear elements, incorporating geometric attributes such as location, direction, and length, as well as the interconnections of these elements. In the case of roads, this information is used to map and analyze traffic data, such as vehicle movements, on the road network. This study aims to propose an area-based spatial analysis method that allows for the flexible application of analysis scales using individual vehicle data, as opposed to node and link generation for linear road networks. The analysis focused on nine expressways, conducting a microscopic analysis of speed-homogeneous sections. The final analysis showed that out of 375 cells, 91 cells in the final 12 division cells did not meet the homogeneity criteria. This discrepancy was ascertained to be due to vehicles decelerating or accelerating when entering or exiting highways at ramps or interchanges, not due to directional speed differences but lane-specific speed variations. The final cells with large speed deviations were found to be influenced by connections to highway on-ramps or off-ramps. In contrast, sections with small speed variations within a cell were influenced by traffic factors such as connection points and traffic volume, which hindered normal driving. As a result, this study validated that traffic information from highways, typically provided as linear data, could be divided into cells based on real-time GPS speed data and presented on an area-based scale. While dividing regions based on fixed intervals does not pinpoint exact speed change points, this study found that reasonable segmentation is possible based on spatial size and speed-homogeneous sections. 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

Intensive lane-changing (LC) events are one of the great causes that make freeway weaving areas become bottlenecks. This study proposes an approach using vehicle trajectory data to investigate the spatiotemporal distributions of the number of LC events, void occupancies, and throughput variations at the freeway weaving area. Firstly, all LC events are extracted from the cleaned dataset and classified into four types according to the LC vehicles’ origin–destination lanes and LC directions. Secondly, the time and space void occupancies are calculated using the kinematic theory. Thirdly, the throughput variations are identified with the oblique N-curve method. Finally, the spatial and temporal distributions of the LC events, void occupancies, and throughput variations are plotted to analyze their characteristics and relationships. The spatial distributions of different types of LC events indicate that most LC events occur at the surrounding area of the on-ramp entrance. Spatial distributions of time void occupancies show that the time void in the original lanes is quite small while that in the target lanes is much larger. Furthermore, the time void occupancies amplify downstream when considering vehicles traveling on the road. By comparing the temporal distributions of LC events, void occupancies, and throughput variations, there is a lag effect between the large value occurrences of space void occupancy and throughput reduction and that of the LC events, which can conclude a causal relationship between LC events and the occurrences of the space void occupancies and throughput reductions. Full article

With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%. Full article

Identifying the bottleneck segments and developing targeted traffic control strategies can facilitate the mitigation of highway traffic congestion. In this study, we proposed a new method for identifying the bottleneck segment in a large highway network based on the percolation theory. A targeted on-ramp control approach was further developed by identifying the major vehicle sources of the bottleneck segment. We found that the identified bottleneck segment played a crucial role in maintaining the functional connectivity of the highway network in terms of meeting the required level of service. The targeted on-ramp control approach can more effectively enhance the service level of the highway network. Full article

This paper deals with the study of variable speed limits (VSLs) for traffic control and their integration with user information strategies. As few studies have addressed the integrated VSL and user information strategy, we focus on comparing the adoption of the latter with the VSL alone strategy application and the no-control case, highlighting the benefits the integration brings. The integrated strategy is able to smooth the severity of congestion, shifting its occurrence in a section of the mainstream mostly suited to vehicle accumulation. An application on a real network is carried out. The traffic congestion conditions along the real highway are simulated by means of Dynameq simulation software and the METANET macroscopic model. The VSLs are applied in a control area aiming to evaluate the potential and the limitations of the strategy on a real network as well as the integration of variable speed limits and user information strategies. Two different cases of road congestion caused by the presence of on-ramps are studied. Results show that the integration of the two strategies leads to a redistribution of flows, achieving a reduction in the total travel time spent in the network and an increase in the traveled distances, i.e., reducing the overall network time despite the increase in assigned flows. However, an integrated strategy requires adequate transportation supply and mainly crossing demand. Full article