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.

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Utilizing Lane-Based Strategy to Incorporate Mixed Traffic in Interchange Control for Connected and Autonomous Vehicles

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119839341 ◽

2019 ◽

Vol 2673 (5) ◽

pp. 454-465 ◽

Cited By ~ 1

Author(s):

Majeed Algomaiah ◽

Zhixia Li

Keyword(s):

Autonomous Vehicles ◽

Penetration Rate ◽

Traffic Signals ◽

Heavy Vehicle ◽

Market Penetration ◽

Mixed Traffic ◽

Traffic Demand ◽

Communication Range ◽

Throughput And Delay ◽

Lane Based

This work examines the next-generation interchange control system (NIC) that aims to control connected and autonomous vehicles (CAV) at interchanges with the consideration of different mixed traffic cases. The first objective of the paper is to test several parameters including traffic demand, heavy vehicle percentage, communication range, and advance stop line (ASL) to investigate their impact on throughput and delay. The second objective is to incorporate mixed traffic in the NIC, utilizing a lane-based strategy that is responsive to market penetration rates. The NIC coordinates vehicles to traverse the interchange terminal by using a reservation-based control strategy with a first-come-first-served (FCFS) reservation protocol. The algorithm of this system was modeled in the simulation software package VISSIM using a slightly modified real-world scenario of interchange. The evaluation of the system starts with testing some key variables when market penetration rate is 100%. The results demonstrate that the increase in traffic demand and heavy vehicle percentage affects the performance of the NIC by increasing the delay. Although the effects of communication range and advance stop location do not have clear patterns, the communication range of 600 ft and ASL of 100 ft indicate a relatively lower delay. Throughput and delay results reveal that the NIC outperforms traffic signals when the market penetration rate is 75%, whereas a 25% market penetration rate provides similar performance to traffic signals.

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Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads

Energies ◽

10.3390/en14123425 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3425

Author(s):

Huanping Li ◽

Jian Wang ◽

Guopeng Bai ◽

Xiaowei Hu

Keyword(s):

Traffic Flow ◽

Autonomous Vehicles ◽

Flow Model ◽

Road Traffic ◽

Autonomous Vehicle ◽

Transformation Model ◽

Hydrodynamic Theory ◽

Traffic Flow Model ◽

Traffic System ◽

The Stability

In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.

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High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽

10.3390/s21020464 ◽

2021 ◽

Vol 21 (2) ◽

pp. 464

Author(s):

Wei Ma ◽

Sean Qian

Keyword(s):

High Resolution ◽

Autonomous Vehicles ◽

Penetration Rate ◽

Data Driven ◽

Traffic Information ◽

Flow Density ◽

Massive Data ◽

Market Penetration ◽

High Resolution Data ◽

Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

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

Applied Sciences ◽

10.3390/app11041514 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1514 ◽

Cited By ~ 2

Author(s):

Quang-Duy Tran ◽

Sang-Hoon Bae

Keyword(s):

Reinforcement Learning ◽

Traffic Congestion ◽

Autonomous Vehicles ◽

Penetration Rate ◽

Autonomous Vehicle ◽

Effective Means ◽

Urban Network ◽

Learning Agents ◽

Policy Optimization ◽

The Impact

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.

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Platoon Signal Priority in Connected-Autonomous Vehicle Environments: Algorithm Development and Testing

Put i saobraćaj ◽

10.31075/pis.65.04.01 ◽

2019 ◽

Vol 65 (4) ◽

pp. 1-9

Author(s):

Milan Zlatkovic ◽

Andalib Shams

Keyword(s):

Traffic Congestion ◽

Autonomous Vehicles ◽

Penetration Rate ◽

Autonomous Vehicle ◽

Transportation Systems ◽

Vehicle Technologies ◽

Measures Of Effectiveness ◽

Urban Corridor ◽

Intersection Control ◽

Day By Day

As traffic congestion increases day by day, it becomes necessary to improve the existing roadway facilities to maintain satisfactory operational and safety performances. New vehicle technologies, such as Connected and Autonomous Vehicles (CAV) have a potential to significantly improve transportation systems. Using the advantages of CAVs, this study developed signalized intersection control strategy algorithm that optimizes the operations of CAVs and allows signal priority for connected platoons. The algorithm was tested in VISSIM microsimulation using a real-world urban corridor. The tested scenarios include a 2040 Do-Nothing scenario, and CAV alternatives with 25%, 50%, 75% and 100% CAV penetration rate. The results show a significant reduction in intersection delays (26% - 38%) and travel times (6% - 20%), depending on the penetration rate, as well as significant improvements on the network-wide level. CAV penetration rates of 50% or more have a potential to significantly improve all operational measures of effectiveness.

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Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives

Sustainability ◽

10.3390/su131911052 ◽

2021 ◽

Vol 13 (19) ◽

pp. 11052

Author(s):

Mohammed Al-Turki ◽

Nedal T. Ratrout ◽

Syed Masiur Rahman ◽

Imran Reza

Keyword(s):

Traffic Flow ◽

Autonomous Vehicles ◽

Penetration Rate ◽

Flow Characteristics ◽

Driving Behavior ◽

Mixed Traffic ◽

Traffic Characteristics ◽

Traffic Environment ◽

Traffic Flow Characteristics ◽

The Impact

Vehicle automation and communication technologies are considered promising approaches to improve operational driving behavior. The expected gradual implementation of autonomous vehicles (AVs) shortly will cause unique impacts on the traffic flow characteristics. This paper focuses on reviewing the expected impacts under a mixed traffic environment of AVs and regular vehicles (RVs) considering different AV characteristics. The paper includes a policy implication discussion for possible actual future practice and research interests. The AV implementation has positive impacts on the traffic flow, such as improved traffic capacity and stability. However, the impact depends on the factors including penetration rate of the AVs, characteristics, and operational settings of the AVs, traffic volume level, and human driving behavior. The critical penetration rate, which has a high potential to improve traffic characteristics, was higher than 40%. AV’s intelligent control of operational driving is a function of its operational settings, mainly car-following modeling. Different adjustments of these settings may improve some traffic flow parameters and may deteriorate others. The position and distribution of AVs and the type of their leading or following vehicles may play a role in maximizing their impacts.

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Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2625-06 ◽

2017 ◽

Vol 2625 (1) ◽

pp. 51-61 ◽

Cited By ~ 23

Author(s):

Parth Bhavsar ◽

Plaban Das ◽

Matthew Paugh ◽

Kakan Dey ◽

Mashrur Chowdhury

Keyword(s):

Traffic Safety ◽

Failure Probability ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Fault Tree ◽

Communication Technologies ◽

Transportation Infrastructure ◽

New Combination ◽

Motor Vehicles ◽

Mixed Traffic

The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.

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Driver Behavior at a Freeway Merge to Mixed Traffic of Conventional and Connected Autonomous Vehicles

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120950721 ◽

2020 ◽

Vol 2674 (11) ◽

pp. 867-874

Author(s):

Sneha Chityala ◽

John O. Sobanjo ◽

Eren Erman Ozguven ◽

Thobias Sando ◽

Richard Twumasi-Boakye

Keyword(s):

Autonomous Vehicles ◽

Driving Simulator ◽

The Elderly ◽

Market Penetration ◽

Mixed Traffic ◽

Traffic Operations ◽

Freeway Traffic ◽

Elderly Drivers ◽

Young Drivers ◽

The Subject

Freeway merge ramps serve as one of the most challenging areas in traffic operations. This paper primarily focuses on creating a mixed traffic of conventional and connected/autonomous vehicles (CAVs) on freeways, and capturing driver behaviors both for the merging vehicle on the ramp and the freeway vehicles. The mixed distribution of vehicle headways of the freeway vehicles, developed based on various market penetration rates of the CAVs, was used to randomly generate vehicles through Monte Carlo simulation, and assigned as headways in a driving simulator. Based on perception, young drivers on the merge ramp were observed to choose critical headway gaps of 2.9 s, 1.8 s, and 1.7 s for freeway traffic of 0%, 50%, 75% penetration rates, respectively. For similar CAV penetration rates, the critical gaps observed for elderly drivers were 3.5 s, 2.0 s, and 1.9 s, respectively. When actually driving in the simulator, for the scenarios of 0% CAVs and 50% CAVs on the freeway, the values of average headway gaps accepted by young drivers were estimated as 2.36 s and 1.53 s, respectively. For the elderly drivers driving the simulator, the average headway gap values accepted were estimated as 2.72 s and 1.55 s, respectively, in the 0% and 50% penetration rates on the freeway traffic. Analyses of the speed profiles of the vehicles showed the effects of the acceleration/deceleration of merging vehicles, for both young and older drivers, on the freeway vehicles, including a few cases of collision. Overall, it was observed that the subject drivers accepted shorter headway gaps for increased CAV penetration levels.

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Trip Cost Estimation of Connected Autonomous Vehicle Mixed Traffic Flow in a Two-Route Traffic Network

Journal of Advanced Transportation ◽

10.1155/2020/8884732 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Zhizhen Liu ◽

Hong Chen ◽

Hengrui Chen ◽

Xiaoke Sun ◽

Qi Zhang

Keyword(s):

Bounded Rationality ◽

Traffic Flow ◽

Cost Estimation ◽

Penetration Rate ◽

Autonomous Vehicle ◽

Flow Simulation ◽

Mixed Traffic ◽

Traffic Condition ◽

Mixed Traffic Flow ◽

Traffic Flow Simulation

With the advancement of connected autonomous vehicle (CAV) technology, research on future traffic conditions after the popularization of CAVs needs to be resolved urgently. Bounded rationality of human drivers is essential for simulating traffic flow precisely, but few studies focus on the traffic flow simulation considered bounded rationality in CAV mixed traffic flow. In this study, we introduce random bounded rationality into the hybrid feedback strategy (HFS) under CAV mixed traffic flow to explore the impacts of CAV penetration rate on the trip cost of vehicles. First, we investigated the bounded rationality of drivers, and we found that it follows normal contribution. Then, we proposed HFS considering random bounded rationality and the CAV penetration rate to simulate the traffic condition. The numerical results show that the enhancement of the CAV penetration rate could reduce total trip cost. The research could help us to simulate the CAVs mixed traffic flow more precisely and realistically.

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Influence of Lane Policies on Freeway Traffic Mixed with Manual and Connected and Autonomous Vehicles

Journal of Advanced Transportation ◽

10.1155/2020/3968625 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Xuedong Hua ◽

Weijie Yu ◽

Wei Wang ◽

Wenjie Xie

Keyword(s):

Autonomous Vehicles ◽

Traffic Management ◽

Heterogeneous Traffic ◽

Mixed Traffic ◽

Freeway Traffic ◽

Average Speed ◽

Traffic Efficiency ◽

Management Measures ◽

Traffic Volumes ◽

Ca Model

Connected and autonomous vehicles (CAVs) have become the highlights of traffic. Researchers in this field have proposed various traffic management measures to enhance the capacity and efficiency of traffic with CAVs, especially mixed traffic of CAVs and manual vehicles (MVs). Exclusive lane setting is included. However, exclusive lane policy-related researches for mixed traffic of CAVs and MVs were very limited, and the influence of number and location of exclusive lanes on the mixed traffic was unclear. To fill this gap, this paper aims to study the influence of different exclusive lane policies on mixed traffic and provide recommended lane policies under various traffic volumes and CAV penetration rates. Freeways with two lanes and three lanes in a single direction were taken into consideration, and sixteen lane policies were proposed. Then different lane policies were simulated with a new proposed cellular automata (CA) model, and properties including flux, average speed, and CAVs degradation were analyzed to evaluate the traffic efficiency of each lane policy. The results show that CAV exclusive lanes can improve the capacity, while MV exclusive lanes seem helpless for capacity improvement. Seven lane policies, including GC, GM, and CM for two-lane freeways and GCG, CGC, and CCM for three-lane freeways, outperform the others in terms of average speed. In addition, exclusive lanes can reduce the probability that CAVs degenerate to AVs. Our findings may help to optimize freeways’ lane policies and improve the efficiency of heterogeneous traffic mixed with CAVs and MVs.

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