Traffic Flow Efficiency Research Articles

Evaluation of Selected Factors Affecting the Speed of Drivers at Signal-Controlled Intersections in Poland

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals.

Hybrid lane change strategy of autonomous vehicles based on SOAR cognitive architecture and deep reinforcement learning

Research on lane change strategies for autonomous vehicles holds paramount importance in optimizing traffic flow efficiency, enhancing driving safety, and adapting to complex traffic environments. While numerous rule-based or machine-learning approaches have been explored to tackle the challenge of lane change on highways, they frequently exhibit limited performance owing to the complexity of driving environments. This study proposes a novel lane change strategy for autonomous vehicles, which utilizes a hybrid framework integrating the SOAR cognitive architecture and deep reinforcement learning (DRL) to address the lane change challenge on highways. First, we introduce a rule extraction algorithm, the RuleCOSI+, which is based on tree ensemble algorithms, designed to extract concise lane change rules from large-scale human driving data. These straightforward rules, together with traffic regulations and safety rules, constitute the long-term memory of the SOAR cognitive architecture, enabling transparent decision-making processes. Next, by analyzing the clipping mechanism of the proximal policy optimization (PPO) algorithm, we propose an Adaptive Clipping PPO (ACPPO) algorithm which is based on the importance of samples. This algorithm adopts different clipping strategies for SOAR samples and ACPPO samples during the training process, enabling the algorithm to more effectively utilize samples with different levels of importance. Then, we propose a hybrid decision-making algorithm: SOAR-ACPPO, which combines the SOAR cognitive architecture with the ACPPO algorithm. This algorithm leverages SOAR’s prior knowledge to effectively and safely guide agent learning. Finally, by selecting appropriate intervention probability and weaning strategy, the system avoids inappropriate knowledge intervention and ensures adequate environment exploration. Simulation experiments conducted using the CARLA simulator illustrate that the proposed strategy not only improves model learning efficiency but also enhances driving efficiency and safety. Additionally, it demonstrates a certain degree of human-like characteristics and interpretability.

A Short-term Vehicle Speed Prediction Approach considering Dynamic Traffic Scene

Vehicle speed serves as a crucial indicator in traffic flow efficiency and safety evaluation. Previous researches typically utilize the time-series data of target vehicle speed or the relative speed and distance between the target and leading vehicles as speed prediction model input, which are suitable for stable and single-vehicle scenes. However, the surrounding traffic scenes dynamically changes during the vehicle’s driving process, the driver needs to allocate attention to the key scene vehicles in the surrounding traffic scenes and adjust the current speed based on these vehicles’ behavior. Therefore, this paper proposes a short-time vehicle speed prediction model considering the dynamic traffic scenes. A dynamic grid method is adopted to divide the target vehicle’s surrounding areas into left front, front, and right front regions. And key scene vehicles are identified based on each region’s state. Then the relative distance, speed, and acceleration data between the target vehicle and key scene vehicles are selected as model inputs. Vehicle trajectory data from the South-North No. 1 Expressway in Hefei and Ranger optimized Temporal Convolutional Network (TCN) are utilized to train the speed prediction model. Experiment results indicated that under 1s, 2s, and 3s prediction lengths, the model’s average Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were 0.430 and 0.271 respectively. Compared to the speed prediction model considering the static traffic scenes, the average RMSE and MAE for the three prediction lengths have been reduced by 49.15% and 56.02% respectively.

Risk identification and prediction model for continuous‐lane‐change vehicles considering driving style

Lane change risk identification and prediction research is important for traffic safety, development of advanced driver assistance systems, traffic flow optimization. Among them, vehicle continuous‐lane‐change (CLC) risk identification and prediction is weak, but this element is important for vehicle decision-making and path planning in multi-lane, non-adjacent lane situations. To compensate for the shortcomings of risk identification based on both deterministic and probabilistic methods and address the issues of poor measurement accuracy and efficiency caused by manual tuning, this study established a risk identification and prediction model based on the Bayesian global optimization (BO) gated recurrent unit (GRU) neural network (BO-GRU) and instantiated the model using the trajectory data of 129 vehicles. Overall, the results showed that the accuracy of identifying CLC risks and driving-style was 99.33% and 97.44%, respectively, while the overall average accuracy of predicting potential CLC risks based on the identification of risks and predicted trajectories was 97.13%. The root-mean-square errors of CLC predicted trajectory for different driving-styles were 0.2503, 0.2331, 0.2093, and 0.2706, respectively, which were 84%−88% lower compared to 1.7361 when driving-styles were not considered. The performance of the BO-GRU model is comparable to that of parameter-optimized LSTM and its variant models, which highlights the significance of parameter optimization for neural network models. Overall, the use of a BO-GRU-based model for identifying and predicting continuous lane change risks in vehicles can aid in making driving decisions and planning routes, decrease the risk of collisions, enhance traffic flow efficiency, and encourage safe driving behavior.

Platoon control and external human–machine interfaces: innovations in pedestrian–autonomous vehicle interactions

At uncontrolled mid-block crosswalks, autonomous vehicles (AVs) are legally and safely obligated to yield to pedestrians, potentially exacerbating traffic congestion. This study explores integrating AV platoon control with eHMIs to examine its long-term potential for enhancing traffic flow safety and efficiency. A novel agent-based pedestrian-vehicle interaction framework is developed to evaluate the proposed strategy, accurately reproducing the heterogeneous behaviors of pedestrians and vehicles, such as pedestrians' risk tolerance, their responses to eHMIs, and vehicles' yielding mechanisms. Additionally, a platoon formation module is proposed to achieve the desired speed and inter-vehicular spacings from arbitrary initial condition. Simulation results indicate that implementing the proposed strategy reduced the conflict rate by 10%, increased the yielding rate by 11%, and decreased aggregate delay by 34.6% in typical flow conditions. The sensitivity analysis reveals that the effectiveness of the proposed strategy in improving traffic safety and efficiency increases with higher pedestrian and vehicle flow rates.

Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting

Traffic flow forecasting is integral to the advancement of intelligent transportation systems and the development of smart cities. This paper introduces a novel model, the Spatial–Temporal Similarity Fusion Graphs Adversarial Convolutional Networks (STSF-GACN), which leverages advanced data preprocessing techniques to enhance the predictive accuracy and efficiency of traffic flow forecasting.The innovation of our approach lies in the meticulous construction of the spatial–temporal similarity matrix through the precise calculation of temporal and spatial similarities. This matrix forms the backbone of our model, serving as the generator in the integrated Generative Adversarial Network (GAN) architecture. The Spatial–Temporal Similarity Fusion Adaptive Graph Convolutional Network, developed as part of our GAN’s generator, utilizes cutting-edge techniques such as the Wasserstein distance and Dynamic Time Warping to optimize the adaptive adjacency matrix, enabling the model to capture latent spatial–temporal correlations with unprecedented depth and precision.The discriminator of the GAN further refines the model by evaluating the accuracy of the traffic predictions, ensuring that the generative model produces results that are not only accurate but also robust against varying traffic conditions. This cohesive integration of GAN into the model architecture allows for a significant improvement in prediction accuracy and convergence speed, moving beyond traditional forecasting methods.

Fractal dimensional analysis to reveal traffic flow dynamics as organic metabolism

As urban areas face rapid population and vehicle growth, understanding system dynamics is crucial for managing transportation congestion effectively. Utilizing fractal and metabolic concepts offers fresh perspectives on the behavior and efficiency of complex urban transportation systems. To understand the dynamics of congested traffic flows, we conducted fractal analysis on the traffic pattern in Seoul city with chronic congestion worldwide and investigated its potential for evaluating metabolic efficiency. Our rank and size fractal analysis revealed that Seoul's fractal dimension ranges from 6.7060 to 7.6801. The fractal dimension's curve, when plotted against traffic congestion, shows a peculiar symmetrical relationship. The linear correlation between fractal dimension and metabolic indicators suggests that fractal dimension could potentially serve as a novel metric for evaluating traffic flow efficiency. This study introduces new insights by integrating fractal and metabolic principles to understand traffic system dynamics, marking the first use of fractal analysis in traffic pattern evaluation.

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.

Assessing Level of Service, Capacity and Future Optimization of Roundabouts in Rajshahi City Using Sidra Intersection

This study investigates traffic flow challenges at the bustling Mintu Chattar roundabout Situated in Laxmipur Mor, Rajshahi, Bangladesh. Located in close proximity to a hub of hospitals and medical establishments, this intersection routinely witnesses traffic congestion, becoming a familiar occurrence in the area. From dawn to dusk, traffic officers are required to step in to manage the traffic flow of countless individuals commuting to hospitals, workplaces, educational institutions, and other destinations. Our capacity analysis using Sidra Intersection software reveals significant bottlenecks, particularly during peak hours, necessitating traffic police intervention due to inadequate control measures. The research identifies key contributing factors: insufficient entry and circulatory lanes, uneven traffic distribution, and high overall traffic volume. A detailed case study of the south leg highlights the severity of the situation, with Level of Service (LOS) dropping to F during evening hours, resulting in long queues (over 61 vehicles) and delays exceeding 82 seconds per vehicle. The integration of the design life model from SIDRA software has also illustrated the correlation between Level of Service (LOS) and capacity with the projected rise in traffic volume for the next five years. In order to improve traffic flow efficiency, the study proposes exploring design optimization of the roundabout. This includes investigating the impact of geometric redesigning, such as gradual expansion of approach lanes width, on capacity at the roundabout leg, considering both existing and future traffic scenarios.

Neural Network based Route Guidance Strategy in intelligent transportation systems

Traffic congestion in urban areas presents a significant challenge to efficient transportation and sustainable urban development. This paper introduces a Neural Network based Route Guidance Strategy to address the non-linear complexities of urban traffic flow. The strategy demonstrates its capability to optimize traffic flow in real-time by predicting the travel times of candidate routes. A comprehensive simulation study compares the performance of the Neural Network Strategy with traditional traffic management strategies, focusing on traffic flow efficiency, vehicle count stability, and route choice optimization. The results indicate that the Neural Network Strategy significantly enhances traffic management by stabilizing vehicle counts, reducing fluctuations in traffic flux, and achieving a more uniform distribution of vehicles. The paper concludes with an analysis of the experimental results, highlighting the integration of neural networks into traffic management systems as a promising approach to mitigating urban traffic congestion. Future research directions are discussed, emphasizing the potential for real-world implementation and the exploration of advanced neural network models.

Smart Traffic Signaling Using Machine Learning and IoT

The project, titled “Smart Traffic Signaling using Machine Learning and IoT," introduces an innovative solution for optimizing traffic signal control. By harnessing the power of image processing, IoT, and machine learning, this project will be a real-time system that accurately assesses vehicle density at intersections. The project focuses on training a machine learning model to recognize various vehicle types, including bikes, cars, trucks, and heavy vehicles. This adaptive control mechanism aims to enhance traffic flow efficiency, reduce congestion, and contribute to the advancement of intelligent transportation systems. systems. Key Words: Machine learning, IoT, Image processing, Smart Traffic.

Streamlining Toll Management: A Survey of Business Requirement Solutions

Effective toll control performs a pivotal role in improving traffic flow, decreasing congestion, and enhancing overall transportation performance. However, toll plaza operators frequently face demanding situations in efficaciously handling statistics and monitoring transactional activities. This studies paper proposes a secure, fast, and dependable statistics control answer tailor-made to the precise wishes of toll plaza operators. By implementing this solution, toll plaza proprietors can successfully track income and loss states, examine regular transaction statistics, and optimize traffic control measures. Through the improvement and implementation of this solution, massive enhancements in traffic flow efficiency and congestion reduction may be achieved, ultimately leading to more advantageous transportation systems. This paper offers a practical method to addressing the complexities of toll control, imparting insights into the advantages and impacts of adopting such answers withinside the transportation sector. Keywords-Toll management, Traffic flow, Congestion reduction, Transportation efficiency, Data management solution, Profit tracking, Transaction analysis, Traffic control, Toll plaza operators, Transportation systems

Traffic flow impact of mixed heterogeneous platoons on highways: an approach combining driving simulation and microscopic traffic simulation

The connected automated vehicle (CAV) platoons are expected to improve the operational performance of the transportation system. Existing platoon testing processes have used the format of mixed heterogeneous platoons (MHPs), where the leader vehicles are connected human-driven vehicles (CHDVs) and the follower vehicles are CAVs. The impact of this type of MHP on existing highway traffic flow is more complex due to the presence of human drivers, and this impact has yet to be analyzed and quantified. Therefore, this paper takes this type of MHP as the research object and conducts experiments by combining driving simulation and microscopic traffic simulation. The driving behavior of the driver of the MHP's leader vehicle is incorporated into the car-following model by means of a car-following model calibration, and an impact analysis is carried out in terms of multiple dimensions of stability, efficiency, and ecological aspects of the mixed traffic flow under different MHP market penetration rates (MPRs). The results show that MHPs weaken the oscillatory effect of mixed traffic flow and improve mixed traffic flow stability. The MPRs of MHPs are proportional to the road capacity and average speed and antipodal to the average travel time. Also, the MHPs help reduce fuel consumption and pollutant emissions (CO2, CO, NOx, HC, and PMx) during traffic operations. This study can provide theoretical support for the future large-scale application and management of MHPs.

Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories

A lane-changing (LC) maneuver may cause the follower in the target lane (new follower) to decelerate and give up space, potentially affecting crash risk and traffic flow efficiency. In congested flow, a more aggressive LC maneuver occurs where the lane changer is partially next to the new follower and creates negative gaps, namely negative gap forced LC (NGFLC). Although NGFLC forms the foundation of sideswipe crashes, little has been done to address its impacts and the contributing factors. To tackle this issue, a total of 15,810 LC trajectory samples are extracted from three drone videos at different locations. These samples are categorized into NGFLC and normal LC groups for comparative analysis. Five commonly used conflict indicators are extended into two-dimensional to evaluate the crash risk of LC maneuver. The change of time gaps during LC maneuver are examined to quantify the impact of LC on traffic flow efficiency. We find that NGFLCs significantly increase crash risk, reflected by the number of hazardous LC events and potential crash areas compared to normal LC. Additionally, results reveal that both the lane changer and the new follower tend to maintain a larger time gap after NGFLCs. Factors including time headway, relative speed, and historical gaps in the target lane significantly affect NGFLC incidence. Once the movement of the leader in the original lane is taken into account, the prediction accuracy improves from 81% to 91%. The transferability tests indicate that the findings about the negative impact of NGFLC and the accuracy of its prediction model are consistent across different locations. These findings hold implications for driving assistance systems to better predict and mitigate NGFLCs.

Impact of Driver Compliance and Aggressiveness in Connected Vehicles on Mixed Traffic Flow Efficiency: A Simulation Study

Connected vehicles (CVs) are becoming increasingly prevalent in today’s transportation systems, and understanding their behavior in mixed traffic flow is crucial for enhancing traffic efficiency and safety. This paper presents a comprehensive study investigating the impact of CV drivers’ compliance and aggressiveness on mixed traffic flow through simulation experiments. The unique contribution of this research lies in the adoption of a clustering method to classify CV drivers’ compliance and aggressiveness based on trajectory data captured by Unmanned Aerial Vehicles (UAVs). This approach allows for the accurate calibration of car-following and lane-changing models, surpassing previous methodologies. The study outlines two primary methods: the intelligent driver model (IDM) with driver compliance (CVs-IDM) and the lane-change 2013 model with drivers’ style. These methods are applied to simulate various scenarios of mixed traffic flow, considering different CV penetration rates and driver types. The pivotal findings reveal that higher CV penetration rates lead to reduced traffic flow disturbance, improved safety, and enhanced efficiency. Specifically, CV drivers exhibiting high compliance and normal aggressiveness demonstrate optimal performance in terms of disturbance reduction, safety, and overall efficiency. This research offers valuable insights for policymakers and practitioners. It recommends increasing the CV penetration rate in mixed traffic flow to enhance overall efficiency. Moreover, selecting the appropriate CV driver type based on the penetration rate can further optimize traffic flow, positively impacting transportation systems and promoting safer and more efficient mixed traffic environments.

Dynamic Lane Assignment and Signal-Timing Collaborative Optimization Considering the Effect of Lane Switching

Dynamic lane assignment (DLA) is an effective method to improve traffic flow and optimize urban road resources. However, lane switching can disrupt traffic under dynamic control due to clearance time and driving characteristics. Traditional rule-based methods hinder the selection of optimal schemes for DLA systems. On-site data analysis shows that lane switching increases saturated headway by 18.4% and reduces lane group utilization by 17.8% compared to fixed lanes in the same direction. To address these challenges, this study proposes an optimization method that integrates DLA and signal timing, considering the impact of lane switching. The proposed model aims to minimize total delay at intersections by optimizing the number and timing of lane switches within a given interval. Case studies and extensive numerical analyses demonstrate that the proposed method outperforms the rule-based approach, reducing total intersection delay by 1.91%. Additionally, the average number of lane switches decreases by 13.8%, reducing safety risks associated with frequent lane switching. Sensitivity analysis reveals the superiority of the proposed method when the utilization rate of variable lanes is below 0.4 and the saturated headway caused by lane switching exceeds 2.9 seconds. Moreover, the proposed method outperforms inductive and adaptive control schemes when the traffic flow deviation coefficient is less than 0.3. This makes it suitable for implementation in cities where drivers have limited experience with variable lanes during the initial stage, when historical and real-time data are more consistent. Overall, the proposed method offers significant improvements in traffic flow efficiency and safety in DLA systems.

OPTIMAL TRAFFIC CONTROL SYSTEM FOR TRAFFIC CONGESTION

A smart city's traffic management system is regarded as one of its primary components. Traffic jams are a common sight on the roadways in metropolitan areas due to the rapid increase in population and urban mobility. In order to address road traffic management issues and assist authorities with appropriate planning, an intelligent traffic management system utilizing the Yolo algorithm and Open CV approach is proposed in this project. A workable model for counting automobiles in traffic was developed using image processing as the basis. image processing methods classified and tallied moving vehicles in traffic scene video streams captured by stationary cameras. The following is the detection and tracking methodology. The adaptive background subtraction technique is initially used to separate the moving vehicles from the traffic scene. Using videos to isolated picture blobs are recognized as individual vehicles once the background is subtracted. Following blob identification, vehicles in a certain area are counted and classified. A count of vehicles was observed with an accuracy of ideal camera calibration. To support the goal, data is gathered from video footage of vehicles traveling toward and away from the camera in order to count and use signal switching. The created system's results demonstrate that, with more enhancements, it applicable to count and categorize vehicles in real-time. After that, an optimization framework makes use of these predictions to dynamically modify signal timings in response to shifting traffic conditions. The optimization method seeks to increase overall traffic flow efficiency, decrease delays, and shorten travel times. To sum up, utilizing real-time traffic data to optimize signal control presents a viable approach to improving urban traffic management. This method makes use yolo techniques to facilitate the creation of flexible and effective traffic control, which in turn helps to create more sustainable and seamless urban transportation networks. KEYWORDS: Image classification, Video tracking, Information analysis, Vehicle detection, Signal Switching.

Air traffic inefficiencies and predictability evaluation using route mapping—the Tokyo International Airport case

Air traffic inefficiencies lead to excess fuel burn, emissions and air traffic controller (ATCo) workload. Various stakeholders have developed metrics to assess the operation performance. Most metrics compare the actual trajectories to some benchmark ones to calculate excess time or distance. This research is inspired by cellular automata (CA) and develops a combined time-distance lateral inefficiency and predictability metric using discrete space and time mapping on published flight routes. The analysis is focused on Tokyo International Airport, but uses only track data and published routes, which makes it easily applicable to any other hub airport worldwide. The mapping and velocity analyses are used to investigate when and where ATCos are most likely to intervene to provide save separation. A metric which can be adjusted to evaluate both traffic flow predictability and efficiency is proposed. This metric can be applied to better understand current traffic and enable future improvements towards seamless air traffic flow management.

Sustainable Urban Mobility for Road Information Discovery-Based Cloud Collaboration and Gaussian Processes

A novel cloud-based collaborative estimation framework for traffic management, utilizing a Gaussian Process Regression approach is introduced in this work. Central to addressing contemporary challenges in sustainable transportation, the framework is engineered to enhance traffic flow efficiency, reduce vehicular emissions, and support the maintenance of urban infrastructure. By leveraging real-time data from Priority Vehicles (PVs), the system optimizes road usage and condition assessments, contributing significantly to environmental sustainability in urban transport. The adoption of advanced data analysis techniques not only improves accuracy in traffic and road condition predictions but also aligns with global efforts to transition towards more eco-friendly transportation systems. This research, therefore, provides a pivotal step towards realizing efficient, sustainable urban mobility solutions.

Uncertainty, Efficiency, and Stability of Mixed Traffic Flow: Stochastic Model-Based Analyses

This paper proposes a stochastic model for mixed traffic consisting of human-driven vehicles (HVs), connected automated vehicles (CAVs), and degraded connected automated vehicles (DCAVs). The model addresses the issue that most of the current literature ignores: the degradation of CAVs, and the heterogeneity and uncertainty of HVs, CAVs, and DCAVs. The source of uncertainty was the heterogeneous behavior of HVs, CAVs, and DCAVs, captured using vehicle-specific car-following relations, that is, parametric uncertainty. The proposed model allowed for the explicit investigation of the uncertainty, efficiency, and stability of mixed traffic under various CAV penetration rates, different positions of CAVs in the traffic stream, and the different degradation levels of CAVs. The numerical experiment results showed that a larger CAV penetration rate helped to reduce uncertainty and improve the efficiency and stability of traffic flow. Furthermore, we investigated the impact of different position combinations of CAVs in the mixed traffic stream on traffic performance under four scenarios: 1) CAVs randomly distributed in the traffic stream, 2) CAVs forming a platoon traveling in the front of the traffic stream, 3) CAVs forming a platoon traveling in the middle of the traffic stream, and 4) CAVs forming a platoon traveling in the rear of the traffic stream. The results demonstrated that Scenario 2 gave the best performance in reducing uncertainty and improving efficiency and stability under different CAV penetration rates, whereas Scenario 4 performed the worst. Moreover, increasing degradation levels of CAVs negatively affected the reduction of uncertainty and improvement of efficiency and stability.