Study on the influence of the proportion of autonomous vehicles on traffic efficiency

2021 ◽  
Author(s):  
Zhixun Lan ◽  
Haiyi Sun ◽  
Zhiming Li ◽  
Shaoqi Shao
Keyword(s):  
Autonomous Vehicles ◽  
Traffic Efficiency

scholarly journals Detection of Road Traffic Anomalies Based on Computational Data Science

2022 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Data Science ◽  
Road Traffic ◽  
Smart Devices ◽  
Traffic Data ◽  
Forecasting Accuracy ◽  
Traffic Efficiency ◽  
Computational Data

Abstract The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long- term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

Using Artificial Potential Field Theory for a Cooperative Control Model in a Connected and Automated Vehicles Environment

2020 ◽  
Vol 2674 (9) ◽  
pp. 1005-1018
Author(s):  
Ziwei Yi ◽  
Linheng Li ◽  
Xu Qu ◽  
Yang Hong ◽  
Peipei Mao ◽  
...  
Keyword(s):  
Potential Field ◽  
Autonomous Vehicles ◽  
Cooperative Control ◽  
Control Model ◽  
Artificial Potential Field ◽  
Traffic Efficiency ◽  
Time Space ◽  
Proposed Model ◽  
Changing Times ◽  
Deceleration Time

Artificial potential field (APF) theory has been extensively applied in traffic path planning as an efficient method to avoid collision. However, studies in collision avoidance based on APF theory only considered the movement of single vehicle. In this paper, a vehicle cooperative control model for avoiding collision in the connected and autonomous vehicles (CAVs) environment is presented, using APF theory. The proposed model not merely guarantees the travel safety of vehicles in avoiding collision, but also promotes driving comfort and improves traffic efficiency. To verify the cooperative control model, simulations of four scenarios are designed and compared with the human driving environment. Five indicators are selected to evaluate the results, that is, time–space diagram, time mean speed (TMS), the rate of large deceleration time (large deceleration is that deceleration larger than –2 m/s2), the inverse time-to-collision ([Formula: see text]), and lane-changing times. According to the simulation results, the cooperative control model could alleviate the capacity drop and increase the TMS to improve traffic efficiency, reduce the rate of the large deceleration time to promote driving comfort, and decrease [Formula: see text] to promote safety in small and large input flow rates. The results reveal the proposed model is significantly superior to the human driving environment whether in free or congested situations, except for the lane-change times, which are slightly larger.

scholarly journals Challenges of Future VANET and Cloud-Based Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Rakesh Shrestha ◽  
Rojeena Bajracharya ◽  
Seung Yeob Nam
Keyword(s):  
Cloud Computing ◽  
Autonomous Vehicles ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Fog Computing ◽  
High Mobility ◽  
Global Knowledge ◽  
Smart Vehicles ◽  
Traffic Efficiency ◽  
Hoc Networks

Vehicular ad hoc networks (VANETs) have been studied intensively due to their wide variety of applications and services, such as passenger safety, enhanced traffic efficiency, and infotainment. With the evolution of technology and sudden growth in the number of smart vehicles, traditional VANETs face several technical challenges in deployment and management due to less flexibility, scalability, poor connectivity, and inadequate intelligence. Cloud computing is considered a way to satisfy these requirements in VANETs. However, next-generation VANETs will have special requirements of autonomous vehicles with high mobility, low latency, real-time applications, and connectivity, which may not be resolved by conventional cloud computing. Hence, merging of fog computing with the conventional cloud for VANETs is discussed as a potential solution for several issues in current and future VANETs. In addition, fog computing can be enhanced by integrating Software-Defined Network (SDN), which provides flexibility, programmability, and global knowledge of the network. We present two example scenarios for timely dissemination of safety messages in future VANETs based on fog and a combination of fog and SDN. We also explained the issues that need to be resolved for the deployment of three different cloud-based approaches.

scholarly journals Designing Maps for Bicycling Simulator Studies – three practical Approaches

2021 ◽  
Vol 4 ◽  
pp. 1-4
Author(s):  
Andreas Keler ◽  
Patrick Malcolm ◽  
Georgios Grigoropoulos ◽  
Klaus Bogenberger
Keyword(s):  
Traffic Control ◽  
Traffic Engineering ◽  
Autonomous Vehicles ◽  
Control Strategies ◽  
Traffic Efficiency ◽  
Road Users ◽  
Key Factor ◽  
Bicycle Infrastructure ◽  
Practical Experiences ◽  
Transportation Research

Abstract. Bicycle simulator studies result from attempts of solving various novel problem statements of modern transportation-related research questions. Examples imply the evaluation of novel traffic control strategies for prioritizing urban bicycle traffic, novel bicycle infrastructure (such as bicycle highways) and the interaction and communication of vulnerable road users with automated or autonomous vehicles. As one of classical disciplines of transportation research, namely traffic engineering, and less related to human factors research, automotive research, geography, urban planning or citizen science, we want to point out those bicycle simulator studies design approaches, which are more related to testing novel traffic control strategies for cyclists, experiencing changing traffic-efficiency and –safety-related parameters in ongoing interfaced microscopic traffic flow simulations. We believe that this is a key factor in experiencing various traffic situations and the evaluation of thereof. In this research, we introduce three practical approaches of how to design maps for bicycling simulator studies. This is mainly resulting from manifold practical experiences from already conducted simulator studies beginning from the year 2018.

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

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.

scholarly journals Safety Evaluation for Connected and Autonomous Vehicles’ Exclusive Lanes considering Penetrate Ratios and Impact of Trucks Using Surrogate Safety Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jian Zhang ◽  
Kunrun Wu ◽  
Min Cheng ◽  
Min Yang ◽  
Yang Cheng ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Safety Evaluation ◽  
Driver Model ◽  
Microscopic Simulation ◽  
Safety Measures ◽  
Traffic Efficiency ◽  
Safety Improvement ◽  
Demand Scenario ◽  
Surrogate Safety Measures

Plenty of studies on exclusive lanes for Connected and Autonomous Vehicle (CAV) have been conducted recently about traffic efficiency and safety. However, most of the previous research studies neglected comprehensive consideration of the safety impact on different market penetration rates (MPRs) of CAVs, traffic demands, and proportion of trucks in mixture CAVs with human’s driven vehicle environment. On this basis, this study is to (1) identify the safety impact on exclusive lanes for CAVs under different MPRs with different traffic demands and (2) investigate the safety impact of trucks for CAV exclusive lanes on mixture environment. Based on the Intelligent Driver Model (IDM), a CAV platooning control algorithm is proposed for modeling the driving behaviors of CAVs. A calibrated 7-kilometer freeway section microscopic simulation environment is built by VISSIM. Four surrogate safety measures, including both longitudinal and lateral safety risk indexes, are employed to evaluate the overall safety impacts of setting exclusive lanes. Main results indicate that (1) setting one exclusive lane is capable to improve overall safety environment in low demand, and two exclusive lanes are more suitable for high-demand scenario; (2) existence of trucks worsens overall longitudinal safety environment, and improper setting of exclusive lanes in high trucks, low MPR scenario has adverse effect on longitudinal safety; and (3) setting exclusive lanes have better longitudinal and lateral safety improvement in high-truck proportion scenarios. Setting one or two exclusive lanes led to [+42.4% to −52.90%] and [+45.7% to −55.2%] of longitudinal risks while [−1.8% to −87.1%] and [−2.1% to −85.3%] of lateral conflicts compared with the base scenario, respectively. Results of this study provide useful insight for the setting of exclusive lanes for CAVs in a mixture environment.

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