Penetration Rate Of Autonomous Vehicles Research Articles

The Impact of Increasing Traffic Volume on Autonomous Vehicles in Roundabout

This study employs the PTV VISSIM simulation software to investigate the impact of increasing traffic volumes on conventional vehicles and autonomous vehicles (AVs) with distinct behavioural traits: cautious, normal, and aggressive. The simulations cover a range of traffic volumes, from 100 to 600 vehicles, and measure the effects on travel time, emissions (CO, NOX, VOC), and fuel consumption. The results show that with increasing penetration rates of AVs, travel times generally decrease, with aggressive AVs achieving the shortest times, followed by normal, then cautious AVs. Emissions and fuel consumption also tend to decrease as the penetration rate of AVs increases. Notably, the results demonstrate that aggressive AVs excel in reducing travel time, while normal AVs consistently balance between efficiency and reduced emissions, and cautious AVs emphasize safety and lower emissions. Despite the differing behavioural traits, all AV types exhibit a marked improvement over conventional vehicles in terms of travel time, emissions, and fuel consumption. At every penetration rate, AVs lead to shorter travel times and lower emissions, with aggressive AVs being the most efficient, followed by normal and then cautious AVs. These findings emphasize the potential benefits of integrating autonomous vehicles into transportation networks. They suggest that optimizing AV behaviour, depending on the context and objectives, can lead to more efficient, environmentally friendly traffic systems. The study offers valuable insights for policymakers, urban planners, and researchers aiming to leverage the distinct strengths of each AV behaviour to create a more sustainable and efficient future for autonomous transportation.

Modeling dedicated lanes for connected autonomous vehicles with poly-information uncertainties and electronic throttle dynamics

Numerous studies have demonstrated that connected autonomous vehicles and human-driven vehicles are now coexisting throughout a transitional phase. Traffic flow can be improved, the system can be stabilized, and less energy will be used with dedicated lanes for connected autonomous vehicles. Additionally, with few communication resources, no communication delivery is ever perfect, leading to issues with poly-information uncertainty. First, this paper provides a macroscopic model of heterogeneous traffic flow from the perspective of vehicle dynamics employing electronic throttle dynamics and poly-information uncertainty with/without dedicated lanes. Second, the mixed traffic flow linear stability criterion is derived using the linear stability theory. The third portion, which was based on the theoretical analysis, focused on the consequences of dedicated lane configurations on traffic flow as well as a discussion of the effects of various parameters on the stability of mixed traffic flow and energy consumption emissions. Finally, we modeled the Huanshan Road in Jinan, China using the experimental VISSIM platform. The analysis and demonstration of a two-way, four-lane road with or without dedicated lanes. The findings demonstrate that increasing connected autonomous vehicles penetration and dedicated lanes construction can increase traffic capacity, enhance the stability of traffic flow, and lower energy use and additional emissions. It is important to keep in mind that dedicated lanes must be built at an appropriate connected autonomous vehicle penetration rate to boost traffic flow without squandering resources.

Modeling the mixed traffic capacity of minor roads at a priority intersection

As technology for autonomous driving continues to advance, a mixed traffic flow comprising of autonomous vehicles (AVs) and regular vehicles (RVs) is gradually taking shape. Priority intersection is an important type of intersection that connects road networks for traffic efficiency. Essentially, vehicles on major road are given the right of way at these priority intersections, while vehicles on minor road must wait for a safe gap to proceed. Thus, the operation of minor road traffic is a crucial factor that can limit the efficiency of priority intersections. This effect becomes even more complex and diverse in mixed traffic flow consisting of both AVs and RVs. This paper proposes a capacity model for the mixed AVs-RVs traffic flow on minor road at priority intersections. To begin with, we mathematically derive control strategies for homogeneous platoons of AVs or RVs as well as heterogeneous platoons of both AVs and RVs with a specific number of AVs on minor road at priority intersection. Following the framework of the control strategies above, the maximum capacity of mixed traffic on minor road can be optimized under each penetration rate of AVs. Furthermore, we take into account headway distribution on major road in the aforementioned control strategies to complete the capacity modeling approach. We then conduct numerical experiments to evaluate the effectiveness of the proposed capacity model in the mixed traffic. Results show that the proposed model can be applied to quantitatively calculate the mixed traffic capacity at priority intersections, and several important factors have significant impacts on the capacity, such as penetration rate of AVs, traffic volume on major road, and headways of both AVs and RVs on minor road. As the penetration rate of AVs increases, it has a positive impact on the capacity of mixed flow on minor roads. However, this effect is offset by negative impacts of traffic volume on major road and the headway of both AVs and RVs. More specifically, when comparing pure AVs traffic to pure RVs traffic on minor roads, we observe a considerable improvement in capacity ranging from 115.82% to 120.89% under various conditions.

MSND: Modified Standard Normal Deviate Incident Detection Algorithm for Connected Autonomous and Human-Driven Vehicles in Mixed Traffic

Advances in IoT and IoV technology have made connected autonomous vehicles (CAVs) data sources. Using CAVs as data sources and in incident management algorithms can create faster, more reliable, and more effective algorithms. This paper proposes a modified standard normal deviation (MSND) incident detection algorithm that uses CAVs as data sources and considers multiple traffic parameters. MSND is utilized in conjunction with two other incident detection algorithms, Standard Normal Deviation (SNS) and California (CAL), in a method of incident management known as Variable Speed Limits (VSL). SUMO Traffic Simulation Software is used to evaluate the effectiveness of the proposed method. A 10.4-kilometer road network is developed. Numerous scenarios are simulated on this road network, with variables including traffic demand, autonomous vehicle penetration rate, incident location, incident length, and incident lane. On the effectiveness metrics of detection rate, false alarm rate, and mean time to detect, simulation results demonstrate that the proposed method outperforms the SND and California methods. In terms of detection rate, the MSND algorithm performs the best, with a 12.27% improvement over the SND algorithm and a 21.99% improvement over the California method. After integrating all incident detection algorithms with the VSL traffic management method and simulating each combination, it was determined that the MSND-VSL integration reduced average density in the critical region by 19.73 percent, followed by SND-VSL with a 13.94 percent reduction and CAL-VSL with a 9.9 percent reduction.

Driving Behaivor Optimization Using Genetic Algorithm and Analysis of Traffic Safety for Non-Autonomous Vehicles by Autonomous Vehicle Penetration Rate

Driving Behaivor Optimization Using Genetic Algorithm and Analysis of Traffic Safety for Non-Autonomous Vehicles by Autonomous Vehicle Penetration Rate

Evaluation of macroscopic fundamental diagram characteristics for a quantified penetration rate of autonomous vehicles

BackgroundThe availability of private vehicles with autonomous features is widespread nowadays. Various car manufacturers are providing attributes like collision warning, city automatic emergency braking, adaptive cruise control, pedestrian detection, lane-keeping assistance and lane departure warning, rear cross-traffic and blind-spot warning in their high-end models.PurposeSuch features can automatically manage the macroscopic fundamental traffic parameters such as speed, headway, etc adaptively. Consequently leading to a heterogeneous traffic stream with diverse car-following behaviour comprising completely manual/traditional (TVs) and autonomous vehicles (AVs). This questions the applicability of classic traffic flow theory relationships on such heterogeneous traffic streams.MethodologyThis paper focuses on developing the macroscopic fundamental diagram for such heterogeneous traffic streams based on the quantified penetration rate (QPR) for autonomous vehicles. The penetration rate is devised by taking into account user demographics, land usage and road network properties. QPR is used as an input for heterogeneous urban traffic stream scenarios to calculate the aggregated urban traffic network dynamics of flow and density for the same network. Travel time versus flow characteristics is evaluated based on calibrated hyperbolic urban link travel time function for both interrupted and uninterrupted flows following the aggregated speed and density output from MFDs for heterogeneous traffic streams. Also, two scenarios are generated for comparison to explain the improvement in the network characteristics together with a sensitivity analysis.ResultsCompared to the base scenario there could be 25-35% of AVs on the road networks based on the analysis in coming fifteen years. This increment in usage impacts the capacity of road networks positively by increasing it up to 59%.ConclusionsResults obtained after the application of the suggested model approach to the real network can be used to define a realistic method for multi-vehicle equilibrium assignment models for heterogeneous traffic streams including autonomous vehicles instead of approximating the penetration rates.

Modeling and analysis of human-machine mixed traffic flow considering the influence of the trust level toward autonomous vehicles

The mixed driving of human-driven vehicles (HVs) and autonomous vehicles (AVs) is an inevitable stage of future traffic developments. As HV drivers have different trust levels toward AVs, interactions between these two vehicle types will lead to discrepant characteristics in HV driving behaviors, which will impact the traffic flow state of expressways. However, few studies have considered this. Based on questionnaire data, this paper analyzes the changing features of the trust level and its influence on driving behaviors. The quantitative model for the trust level is constructed using the fuzzy logic approach. On this basis, classical cellular automaton models are improved to reflect the features of human-machine mixed traffic flow. Finally, correlations between the trust level and the influencing factors are analyzed, and the impact of the trust level on traffic flow operations is described in terms of both efficiency and safety. The questionnaire results reveal that the influence of the trust level on driving behaviors is universal. The trust level varies with personal and contextual attributes. The simulation results show that traffic density and road congestion greatly influence the trust level. However, the trust level is not sensitive to changes in the penetration rate of AVs. Interactions between these two vehicle types are stronger when the penetration rate approaches 50%. The efficiency and safety of traffic flow operations under each condition decrease by different magnitudes from the influence of the trust level.

A Queuing Model for Mixed Traffic Flows on Highways considering Fluctuations in Traffic Demand

Modelling the mixed traffic flows of autonomous vehicles (AVs) and human-driven vehicles (HVs) on highways is challenging. Randomness, fluctuations, and congestion exist in the mixed traffic flows. This paper extends the current literature by proposing an M/G(n)/c/c state-dependent queuing model operating in a random environment. The fluctuating traffic demand is addressed by arrival rates modulated by the random environment. Meanwhile, a Markovian arrival process (MAP) is incorporated to describe the platoons. We investigate the performance of the mixed traffic flow under the I policy (AVs and HVs travel together in all lanes) and the D policy (one lane is designated to AVs). Numerical experiments reveal the following interesting findings: (1) the fluctuation degree of traffic demand, the traffic intensity, and the penetration rate of AVs play essential roles in determining the performance of mixed traffic flows. (2) The I policy should always be adopted if the travel time is more valuable. In terms of output rate, the choice between the I and the D policies depends on the traffic intensity, SCV of arrival rates and penetration rate. (3) A larger penetration rate is required to completely eliminate congestion on a longer highway segment.

Simulating and Analyzing aggressive car-following behavior for testing autonomous vehicles

Abstract In a mixed traffic flow, evaluating the operation safety of autonomous vehicles (AVs) is crucial under different aggressive car-following behavior of surrounding human driven-vehicles (HVs). To pursue this goal, this paper develops a machine-learning-based simulation method accompanied by a traditional car-following model. We integrate the unsupervised learning method, Approximate Bayesian computation, and Gipps car-following model to obtain different parameter distributions of the Gipps model. After utilizing the key parameter distribution, this paper employs a supervised learning method to predict the aggressive index of human-driven vehicles in each individual car-following event. Further, we verify the outputs of this simulation model with the NGSIM I-80 traffic dataset. After the validation, we develop an AV simulation study to analyze Avs’ performance in different aggressive HVs scenarios. The result indicates that the higher penetration rate of AV is essential for stabilizing AVs’ performance in terms of velocity and the probability of involving in a crash. Additionally, aggressive HV drivers significantly impact scenarios of low AV penetration rate regarding safety issues.

Lane change behavior with uncertainty and fuzziness for human driving vehicles and its simulation in mixed traffic

In the process of constructing the driver’s lane-changing model, in addition to the dynamic states of the surrounding vehicles affecting the driver’s lane-changing decision, various traffic environment factors and the driver’s own personality also affect the driver’s lane-changing probability. At the same time, these influencing factors also make the lane changing process ambiguous and uncertain. To address these issues, in this study the membership function of soft set is applied to quantify the impact of some typical ambiguous factors and relationships, such as weather conditions, driver personality, and vehicle type, on the driver’s driving process. The mapping relationships between these influencing factors and the driver’s expected speed and aggression are obtained through decision rules. Then a new lane changing model considering uncertain or ambiguous factors and relationships is proposed. On the basis of this model, simulation experiments of mixed traffic flow under the conditions of 0%, 20%, 40% and 60% penetration rate of autonomous vehicles were carried out. The experimental results reasonably present the impact of autonomous vehicle penetration and some ambiguity on the lance change behavior.

Influence of Lane-Changing Behavior on Traffic Flow Velocity in Mixed Traffic Environment

In mixed traffic with autonomous vehicles, the relationship between speed and lane-changing behavior is an important basis for mixed traffic control. In this study, we use empirical, simulation, and data-driven methods to study the relationship between speed and lane change rates in mixed traffic under different autonomous vehicle penetration rates. We use the empirical data to establish the corresponding road simulation models. Based on the simulation model, the traffic flow simulation experiments under the conditions of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% penetration rate of autonomous vehicles were carried out. The analysis of the simulation results found that: (1) the penetration of autonomous vehicles into the road has a positive impact on the lanes far away from the entrance and exit, while the impact on the lanes closer to the entrance and exit is not obvious. (2) Lane-changing behavior has effectively decreased with the penetration of autonomous vehicles, but it is not obvious when the penetration rate exceeds 10%, and there is no significant drop in the lane connecting the entrance and exit. (3) There is a linear relationship between speed and lane-changing rate. Under different penetration rates, the data-driven analysis is used to perform multiple linear regressions, and the regression formula fits are all above 0.7. Based on the above findings, the linear formula of the fitting is proposed, and the value interval of the parameters in different states is given as well. Due to the small changes in the parameter values under different permeability conditions, the model has a certain degree of stability. The speed-lane change rate model proposed in this study can better describe the relationship between the speed of the ring-shaped urban expressway and the lane-changing behavior in the mixed traffic environment with the larger traffic flow.

An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

Analytical approximation for macroscopic fundamental diagram of urban corridor with mixed human and connected and autonomous traffic.

Advances in connected and autonomous vehicles have the promise to reshape the future of the transportation system. How and when the benefits associated with automation and connectivity technology will start to impact the performance of an urban corridor is an issue of interest for traffic operators. This paper proposes an analytical capacity model for urban corridors with mixed traffic based on the concept of macroscopic fundamental diagram. The model incorporates the full spectrum of connected and autonomous vehicle penetration rates as well as the reaction times of different vehicle following patterns. The connected and autonomous vehicle platoon intensity, formulated as an exponential function of the connected and autonomous vehicle penetration rate, is also considered in the proposed analytical capacity model. Numerical experiments are conducted to verify that different reaction time settings yield disparate results. Some reaction time settings were found to cause the corridor capacity to increase monotonically with the connected and autonomous vehicle penetration rate while others led to decreases in corridor capacity with connected and autonomous vehicle penetration rates. Finally, the validity of the proposed methodology is verified via simulation tests in VISSIM 2020.

Proximal Policy Optimization Through a Deep Reinforcement Learning Framework for Multiple Autonomous Vehicles at a Non-Signalized Intersection

Advanced deep reinforcement learning shows promise as an approach to addressing continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a deep reinforcement-learning-based model that considers the effectiveness of leading autonomous vehicles in mixed-autonomy traffic at a non-signalized intersection. This model integrates the Flow framework, the simulation of urban mobility simulator, and a reinforcement learning library. We also propose a set of proximal policy optimization hyperparameters to obtain reliable simulation performance. First, the leading autonomous vehicles at the non-signalized intersection are considered with varying autonomous vehicle penetration rates that range from 10% to 100% in 10% increments. Second, the proximal policy optimization hyperparameters are input into the multiple perceptron algorithm for the leading autonomous vehicle experiment. Finally, the superiority of the proposed model is evaluated using all human-driven vehicle and leading human-driven vehicle experiments. We demonstrate that full-autonomy traffic can improve the average speed and delay time by 1.38 times and 2.55 times, respectively, compared with all human-driven vehicle experiments. Our proposed method generates more positive effects when the autonomous vehicle penetration rate increases. Additionally, the leading autonomous vehicle experiment can be used to dissipate the stop-and-go waves at a non-signalized intersection.

Optimum Location of Autonomous Vehicle Lanes: A Model Considering Capacity Variation

This paper proposes a model to find the optimal location of autonomous vehicle lanes in a transportation network consisting of both Autonomous Vehicles (AVs) and Human-Driven Vehicles (HDVs) while accounting for the roadway capacity variation. The main contribution of the model is considering a generalized definition of capacity as a function of AV proportion on a link and incorporating it into the network design problem. A bilevel optimization model is proposed with total travel time as the objective function to be minimized. At the upper-level problem, the optimal locations of AV lanes are determined, and at the lower level which is a multiclass equilibrium assignment, road users including both AVs and HDVs seek to minimize their individual travel times. It is shown that if capacity variation is ignored, the effect of AV lane deployment can be misleading. Since there will be a long transition period during which both AVs and HDVs will coexist in the network, this model can help the network managers to optimally reallocate the valuable road space and better understand the effects of AV lane deployment at the planning horizon as well as during the transition period. Employing this model as a planning tool presents how the proposed AV lane deployment plan could consider the AV market penetration growth during the transition period. Numerical analysis based on the Sioux Falls network is presented in two cases with and without variable capacity to illustrate the application of this model. At the 60% penetration rate of AVs, the improvement in total travel time was 3.85% with a fix capacity while this improvement was 9.88% with a variable capacity.

Mixed flow of autonomous and human-driven vehicles: Analytical headway modeling and optimal lane management

Presence of autonomous vehicles (AVs) affects traffic flow characteristics of a mixed traffic stream comprising human-driven vehicles. To model the impact of AVs on the saturation flow of arterials and highways, we propose analytical models to derive the expected value and variance of headway of a traffic stream with mixed AVs and conventional human-driven (or normal) vehicles (NVs), given the expected penetration rate of AVs. The proposed model considers the arrangement (order) of AVs and NVs in the mixed stream and the expected, the lowest, and the highest achievable headways and their variability are analytically derived. Moreover, the total delay of a two-lane road with interrupted traffic flow is analytically modeled for various possible lane allocation policies: (a) dedicated lanes, (b) mixed-mixed lanes, (c) mixed-NV lanes, and (d) mixed-AV lanes. Microsimulation experiments demonstrate the validity of the developed models for the average headway and its variability, as well as the delay formulas.

Can autonomous vehicle reduce greenhouse gas emissions? A country-level evaluation

Autonomous vehicles are believed to be the next-generation technology for future societies. The energy and environmental impacts of autonomous vehicles have been realized to be important issues, but quantitative analysis is lacking. In this study, by using China's passenger vehicle fleet as an example, we evaluate the effects of autonomous vehicle deployment on greenhouse gas emissions in different scenarios of autonomous vehicle penetration rates and fuel consumption changes. A comprehensive literature review is conducted to support the study. Autonomous vehicles are found to potentially affect the total greenhouse gas emissions in multiple ways, including reducing vehicle ownership, increasing vehicle use intensity, and changing the vehicle fuel consumption rate. These impacts are mostly internally offset such that the overall impact of autonomous vehicle deployment on greenhouse gas emissions is not significant in the near-to mid-term. With a higher autonomous vehicle penetration rate achieved, in the optimistic scenario, a net reduction in greenhouse gas emissions is expected to be realized. In addition, the fuel economy levels of autonomous vehicles are highly uncertain and cause major uncertainties in the simulation results. More field tests and evidence are needed to improve the evaluation reliability.

Impact of Autonomous-Vehicle-Only Lanes in Mixed Traffic Conditions

The introduction of autonomous vehicles (AVs) in the near future will have a significant impact on road traffic. AVs may have advantages in efficiency and convenience, but safety can be compromised in mixed operations of manual vehicles and AVs. To deal with the issues associated with mixed traffic and to avoid its negative effects, a special purpose lane reserved for AVs can be proposed to segregate AVs from manual vehicles. In this research, we analyze the effect on efficiency and safety of AVs in mixed traffic and in a situation where an AV-only lane is deployed. In the analysis, we investigate the average speed, the throughput, and the inverse time-to-collision (ITTC). We differentiate the behaviors of manual vehicles and AVs through the reaction time, desired speed, and car-following models. As a result, we observe that the efficiency is improved when the market penetration rate of AVs increases, especially when the highway throughput increases by up to 84% in the case of mixed traffic. However, safety worsens when the market penetration of AVs is under 40%. In this case, the average speed can be improved and the frequency of dangerous situations (ITTC > 0.49) can be reduced drastically in the merging section by making the innermost lane AV-only. Accordingly, we conclude that AV-only lanes can have a significant positive impact on efficiency and safety when the market penetration rate of AVs is low.

Parking Pricing and Design in the Morning Commute Problem with Regular and Autonomous Vehicles

Autonomous vehicles can profoundly change parking behavior in the future. Instead of searching for parking, autonomous vehicle owners get dropped off at their final destination and send their occupant-free cars to a parking spot. In this paper, we study the impact of parking in a bi-class morning commute problem with autonomous and regular vehicles. We consider a spatial distribution of parking spaces, which allows us to capture the parking location of travelers. In the equilibrium condition, autonomous vehicles leave home later (than regular vehicles), and park farther away from their destination. The regular vehicle travelers, however, leave home sooner to park closer to the destination with a smaller walking distance. The reverse occurs if regular travelers abdicate walking and take a faster mode from their parking space to the city center. To optimize the system, we develop temporal and spatial parking pricing strategies and a new parking supply design scheme, as practical alternatives for the conventional dynamic congestion pricing. The proposed parking pricing strategies incentivize commuters to adjust their travel schedules by charging a parking price that increases with distance from the city center. Hence, those who park closer to their destination have to pay less. This is a counter-intuitive finding at first which arises from a trade-off with the earliness penalty of users who park close to the destination. We show that system optimality is also reached by redistributing the parking supply. Our numerical experiments capture the impact the autonomous vehicle penetration rate on the performance of the system and the proposed parking pricing and design strategies.

Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures

Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).