Comparing Dynamic Traffic Flow Between Human-Driven and Autonomous Vehicles Under Cautious and Aggressive Vehicle Behavior

To explore the impact of autonomous vehicles (AVs) on human-driven vehicles (HDVs), a solution for AV to coexist harmoniously with HDV during the car following period when AVs are in low market penetration rate (MPR) was provided. An extension car following framework with two possible soft optimization targets was proposed in this article to improve the experience of HDV followers with different following strategies by deep deterministic policy gradient (DDPG) algorithm. The pretreated Next Generation Simulation (NGSIM) dataset was used for the experiments. 1027 car following events with being redefined were extracted from it, in which 600 of the events were used for training and 427 of the events were used for testing. The different driving strategies obtained from the classical car following models were embedded into virtual environment built by OpenAI gym. The reward function combined safety, efficiency, jerk, and stability was used to encourage the agent with DDPG algorithm to maximize it. The final result reveals that disturbance of HDV followers decreases by 2.362% (strategy a), 8.184% (strategy b), and 13.904% (strategy c), respectively. The disturbance of HDV follower decreases by 14.961% (strategy a), 12.020% (strategy b), and 13.425% (strategy c), respectively. HDV followers with different strategies get less jerk in both soft optimizations. AV passengers get a loss on jerk and efficiency, but safety is enhanced. Also, AV car following performs better than HDV car following in both soft and brutal optimizations. Moreover, two possible solutions for harmonious coexistence of HDVs and AVs when AVs are in low MPR are proposed.

Traffic & environmental impact assessment under distinct operational speeds for automated shuttle bus services

Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial

Impact of CAV platoon management on traffic flow considering degradation of control mode

Automated and partially automated vehicles will substantially change the way traffic flows, even at low market penetration rates. While the primary tool for analysis of mixed human-piloted and automated traffic has been microscopic simulation, the use of macroscopic traffic simulation allows for larger-scale simulations. With that in mind we propose a simple numerical method to estimate the composite fundamental diagram of mixed human-piloted and automated traffic flow from the fundamental diagrams of 100% human-piloted and 100% automated traffic flow and conduct calibrated microscopic traffic simulations to produce fundamental diagrams for how mixed traffic flow may behave at different market penetration rates. This method is demonstrated for both an automated vehicle and a commercially available adaptive cruise control vehicle. The proposed method is shown to be able to accurately estimate the shape of the fundamental diagram for arbitrary automated vehicle market penetration rates and the results provide insight into how the mixed traffic flow may behave at the macroscopic level.

SWSCAV: Real-time traffic management using connected autonomous vehicles

The macroscopic evaluation of road traffic characteristics is vital as they will alter significantly with the emergence of Connected and Autonomous Vehicles (CAVs). The macroscopic second-order traffic flow models are emerging as essential tools to observe the evolutionary dynamics of traffic flow. These models are significantly improved to describe the dynamics of traffic flow in view of Human-Driven Vehicles (HDVs). However, proficient macroscopic second-order traffic flow models are indispensable in comprehending and elucidating the descriptions of traffic flow dynamics, particularly in the view of evolving CAVs. Therefore, we propose a macroscopic second-order traffic model that distinguishes CAVs from HDVs by meticulously considering their distinct driving characteristics. The proposed model incorporates lengthways between the vehicles, and the reaction time of the CAVs, and is evaluated on a 20,000 m ring road. The findings show that with smaller lengthways and reaction times of CAVs, traffic evolves quickly during transitions. An increase in average traffic velocity is observed as the reaction time is significantly small. It has been demonstrated that CAVs can significantly limit variations in velocity resulting in smoother flow operations.

This study proposes an optimal control method for connected autonomous vehicles (CAVs) through signalized intersections to reduce the energy consumption of mixed human-driven vehicles (HDVs) and CAV traffic. A real-time optimal control model was developed to optimize the trajectory of each CAV by minimizing energy consumption during the control period while ensuring traffic efficiency and safety. The control conditions of the CAVs were analyzed under different driving scenarios considering the impact of signal phase timing and preceding vehicles. Additionally, a method is proposed for CAVs to guide other vehicles directly and reduce the energy consumption of the entire signalized intersection. Simulation experiments using MATLAB and SUMO were conducted to evaluate the performance of the proposed method under various traffic conditions, such as different levels of saturation, market penetration rates (MPRs), and the green ratio. The performance was measured using average energy consumption and an average time delay. The results show that the proposed method can effectively reduce vehicle energy consumption without compromising traffic efficiency under various conditions. Moreover, under traffic saturation, the proposed method performs better at a high MPR and green ratio, especially at 40–60% MPR.

Previous empirical behavior analysis on Autonomous Vehicles (AV) mainly focused on vehicles with Adaptive Cruise Control (ACC) system due to the lack of high-level AV dataset. Recently released SAE Level-4 AV datasets such as the Waymo Open Dataset provide great opportunities to evaluate their behavioral impact on traffic flow. In this study, we aim to characterize the empirical Car Following (CF) behaviors of the Waymo autonomous vehicle and compare its feature with human-driven Vehicles (HV), and capture such behavioral differences using the IDM CF model. Our main findings include: (a) AV is much safer than HV, based on our analysis using surrogate safety measures, as time headways and jam spacings of the AV are significantly larger than HV; (b) the response time of AV is also significantly larger than that of HV in response to various types of stimuli; (c) despite the short length of trajectories in the Waymo Open Dataset, we have confirmed that these trajectories are suitable for calibrating some of the IDM parameters; and the calibration results of IDM are consistent with our empirical analysis. Moreover, the modelling results, reveal that the proportion of string unstable behavior of AV is less than that of HV; and (d) for HV, there is generally no significant difference between following AV and following HV except a smaller jam spacing when following AV. Overall, we conclude that currently AV behaves in a conservative way to ensure its safety at the cost of traffic efficiency.

Vehicle incidents on roads result in lane closure and severe traffic congestion, and the frequent mandatory lane changes of the upstream vehicles generate capacity drops ahead of the incidents, which further increase road congestion. With the development of connected vehicles, vehicle incidents can be detected by individual vehicles, and immediate driving assistance can be provided to help them pass the incidents efficiently. This paper proposes a distributed lane-changing assistant (DLCA) system with connected vehicles to advise individual vehicles with the optimal lanes to pass incidents with smaller delays. The system introduces connected vehicles to detect the location and the lane closure information of an incident and broadcast the information to the upstream connected vehicles. To determine the optimal lane for each connected vehicle, a speed index is defined for each lane based on the incident information and the downstream connected vehicle dynamics. The DLCA system is evaluated with a microscopic traffic simulator, INTEGRATION, to illustrate its benefits in improving the performance of individual vehicles and mitigating road congestion. A sensitivity analysis of market penetration rates and demand levels of connected vehicles is also conducted in this paper. The results indicate that the DLCA system can reduce the delay by about 22.1% for the connected vehicles, and it has higher benefits on improving the performance of the entire road at higher market penetration rates. In addition, there exists an optimal demand level to maximize the benefits of the system.

Mixed traffic capacity estimation of autonomous vehicles impact based on empirical data

The emergence of Automated Vehicles (AVs) will result in mixed traffic with Human-Driven Vehicles (HDVs), creating a complex traffic environment. While literature supports the effect of AVs on human driving behavior, the influence of different AV driving styles in this context remains largely unexplored. This study investigated interactions between HDVs and AVs varying in driving style (cautious vs. aggressive) and appearance (recognizable vs. unrecognizable) at Market Penetration Rates (MPR) of 0% to 75% in 25% increments within a highway context. Car-following behavior of 160 participants (56 females, age range 19-65) in a driving simulator experiment was analyzed using average time headway (THW) and standard deviation of relative speed (SDRS) as key metrics. Linear Mixed Models (LMMs) and Structural Equation Modelling (SEM) were applied. Results showed that compared to the baseline (no AVs), drivers' THW decreased when interacting with aggressive AVs by 11.1%, 8.6%, and 13.2% at MPRs of 25%, 50%, and 75%, respectively. Interactions with cautious AVs led to even larger reductions in THW by about 9.9%, 16.6%, and 17.3% at the same MPRs. SDRS improved across MPRs by 4.5%, 5.2%, and 6.2%, independent of AV driving style or appearance. SEM analysis indicated that AV driving style moderated relationships between independent, mediating, and dependent variables, while drivers' perceived stress and comfort mediated effects of AVs, trust propensity, and recognizability on THW and SDRS. These findings provide insights into human responses to AV behavior, highlighting implications for traffic flow and safety to ensure smoother and safer human-AV interactions.

The authors present the real-time three-dimensional (3D) traffic simulator RASY (Real-time autonomous vehicle traffic simulator developed by Yonsei University), for the modeling and analysis of various congestion scenarios, including interactions among human-driven and autonomous vehicles. Vehicles with different control models can be generated to investigate the effects of vehicle models and traffic environment on the traffic flow during simulations of congestion. A data log of the simulation can be exported for post-processing analysis. Following an analysis of simulation data under congested conditions, the authors found that a faster reaction time of an individual vehicle can raise the overall flow rate of a road, raising the critical vehicle density for congestion.

Human-driven Vehicles (HVs) and Autonomous Vehicles (AVs) will inevitably coexist in the future, resulting in mixed traffic conditions. Modeling car-following behavior in such mixed traffic is crucial for better understanding the differences in driving behavior between AVs and HVs, thereby facilitating the development of AV driving strategies to enhance traffic safety. In such mixed traffic, the car-following modes typically include AVs following HVs, HVs following HVs and HVs following AVs. Previous studies have compared the driving behavior of HVs following HVs and HVs following AVs, but few have considered the driving behavior of AVs following HVs in the comparison. To bridge this gap, this paper first decomposes car-following events in mixed traffic extracted from a Lyft level-5 dataset into driving primitives using the Hidden Markov Model with Gaussian Mixture Model (GMM-HMM). Then, the Latent Dirichlet Allocation with GMM (GMM-LDA) is introduced to cluster the driving primitives into different car-following behavior patterns. Based on the identified patterns, the differences across the three car-following modes are further explored. The results show that the driving behavior under three car-following modes differs from each other. Specifically, compared to the HVs following HVs mode, in the AVs following HVs mode, AVs tend to maintain car-following behavior patterns with a longer distance gap and adopt a lower speed to ensure safety. This study offers a comprehensive understanding of car-following behavior in mixed traffic, which will facilitate the development of human-like driving strategies for AVs and enhance the safety of mixed traffic.

Automated vehicles (AVs) are expected to shape the future of transportation and to improve traffic flow and safety. Studies have focused on AVs effects on traffic flow during the transition to full automation, with few examining their influence on human-driven vehicles (HDVs). This study investigated potential changes in HDVs’ driving behavior induced by the presence of AVs with different driving styles (aggressive vs. cautious) at varying market penetration rates (MPRs) (0%, 25%, 50%, and 75%). First, a driving simulator experiment with 160 people (56 females, 104 males) was conducted to collect HDV trajectory data. Then, a microsimulation model was implemented in VISSIM, where HDV behavioral parameters were calibrated using the driving simulator data. Average time headway (THW), relative velocity (RelVel), average acceleration (Acc), average deceleration (Dec), and lane change frequency (LnCh) were used as behavioral metrics. A two-way ANOVA was applied for analysis. Results showed that higher AVs’ MPRs decreased THW, Acc, and Dec in HDVs, while RelVel increased with cautious AVs and decreased with aggressive AVs. Similar trends were observed for LnCh. These findings highlight the need to consider potential HDVs behavioral adaptation during the transition phase, as neglecting it may lead to inaccurate traffic assessments and ineffective policies.