scholarly journals Macroscopic Traffic Simulation of Autonomous Vehicle Effects

Vehicles ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 187-196
Author(s):  
Mohammed Obaid ◽  
Arpad Torok
Keyword(s):  
Autonomous Vehicles ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Road Transportation ◽  
Total Delay ◽  
Urban Road ◽  
Vehicle Mobility ◽  
Network Speed ◽  
Restrictive Measures

The increasing worldwide demand on urban road transportation systems requires more restrictive measures and policies to reduce congestion, time delay and pollution. Autonomous vehicle mobility services, both shared and private, are possibly a good step towards a better road transportation future. This article aims to study the expected impact of private autonomous vehicles on road traffic parameters from a macroscopic level. The proposed methodology focuses on finding the different effects of different combinations of autonomous vehicle penetration and Passenger Car Units (PCU) on the chosen road traffic model. Four parameters are studied: traveled daily kilometers, daily hours, total daily delay and average network speed. The analysis improves the four parameters differently by implementing autonomous vehicles. The parameter total delay has the most significant reduction. Finally, several mathematical models are developed for the percentage of improvement for each chosen parameter.

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

Energies ◽  
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.

Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment

2021 ◽  
pp. 036119812199347
Author(s):  
Lei Lin ◽  
Siyuan Gong ◽  
Srinivas Peeta ◽  
Xia Wu
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Road Transportation ◽  
Trajectory Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of HDVs and CAVs. Equipped with multiple sensors and vehicle-to-vehicle communications, a CAV can track surrounding HDVs and receive trajectory data of other CAVs in communication range. These trajectory data can be leveraged with recent advances in deep learning methods to potentially predict the trajectories of a target HDV. Based on these predictions, CAVs can react to circumvent or mitigate traffic flow oscillations and accidents. This study develops attention-based long short-term memory (LSTM) models for HDV longitudinal trajectory prediction in a mixed flow environment. The model and a few other LSTM variants are tested on the Next Generation Simulation US 101 dataset with different CAV market penetration rates (MPRs). Results illustrate that LSTM models that utilize historical trajectories from surrounding CAVs perform much better than those that ignore information even when the MPR is as low as 0.2. The attention-based LSTM models can provide more accurate multi-step longitudinal trajectory predictions. Further, grid-level average attention weight analysis is conducted and the CAVs with higher impact on the target HDV’s future trajectories are identified.

scholarly journals Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Qiyi He ◽  
Xiaolin Meng ◽  
Rong Qu
Keyword(s):  
Cyber Security ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Assessment Method ◽  
Transportation Systems ◽  
Cyber Attacks ◽  
Large Set ◽  
Security Attacks ◽  
Physical Damage

CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments have researched extensively on the development of CAVs. The increasing number of autonomous and connected functions however means that CAVs are exposed to more cyber security vulnerabilities. Unlike computer cyber security attacks, cyber attacks to CAVs could lead to not only information leakage but also physical damage. According to the UK CAV Cyber Security Principles, preventing CAVs from cyber security attacks need to be considered at the beginning of CAV development. In this paper, a large set of potential cyber attacks are collected and investigated from the aspects of target assets, risks, and consequences. Severity of each type of attacks is then analysed based on clearly defined new set of criteria. The levels of severity for the attacks can be categorized as critical, important, moderate, and minor. Mitigation methods including prevention, reduction, transference, acceptance, and contingency are then suggested. It is found that remote control, fake vision on cameras, hidden objects to LiDAR and Radar, spoofing attack to GNSS, and fake identity in cloud authority are the most dangerous and of the highest vulnerabilities in CAV cyber security.

Study of Setting of Bus Bay Stop Based on VISSIM

2014 ◽  
Vol 641-642 ◽  
pp. 906-909
Author(s):  
Jian Jun Wang ◽  
Xin Ting Huang ◽  
Ning Zhao
Keyword(s):  
Traffic Flow ◽  
Delay Time ◽  
Road Traffic ◽  
Microscopic Simulation ◽  
Total Delay ◽  
Urban Road ◽  
Bus Stop ◽  
Road Design ◽  
Bus Station ◽  
Road System

Setting state of bus stop affects the efficiency of the entire road system. Through discussing bus bay stop’s width, length and distance to intersection, a microscopic simulation was conducted in this paper. With a certain bus frequency, the total delay time of vehicles was analyzed under different road traffic in different forms of Da Yanta Bus Station in Xi’an. The results show that setting bus bay stops on the secondary roads which have large traffic flow can significantly reduce delay time, and can provide references for urban road design.

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

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.

scholarly journals A Prediction Method of GHG Emissions for Urban Road Transportation Planning and Its Applications

2020 ◽  
Vol 12 (24) ◽  
pp. 10251
Author(s):  
Jing Gan ◽  
Linheng Li ◽  
Qiaojun Xiang ◽  
Bin Ran
Keyword(s):  
Carbon Emissions ◽  
Transportation Planning ◽  
Road Traffic ◽  
Ghg Emissions ◽  
Prediction Method ◽  
Accurate Estimation ◽  
Low Carbon ◽  
Road Transportation ◽  
Urban Road ◽  
Carbon Control

The increasing vehicle usage has brought about a sharp increase in greenhouse gas (GHG) emissions of vehicles, which brings severe challenges to the sustainable development of road transportation in Chinese counties. Low-carbon transportation planning is an essential strategy for carbon control from the source of carbon emissions and is crucial to the full transition to a low-carbon future. For transportation planning designers, a quick and accurate estimation of carbon emissions under different transportation planning schemes is a prerequisite to determine the optimal low-carbon transportation development plan. To address this issue, a novel prediction method of hourly GHG emissions over the urban roads network was constructed in this paper. A case study was conducted in Changxing county, and the results indicate the effectiveness of our proposed method. Furthermore, we applied the same approach to 30 other counties in China to analyze the influencing factors of emissions from urban road networks in Chinese counties. The analysis results indicate that the urban road mileage and arterial road ratio are the two most important factors affecting road network GHG emissions in road traffic planning process. Moreover, the method was employed to derive peak hour emission coefficients that can be used to quickly estimate daily or annual GHG emissions. The peak hour emission of CO2, CH4, and N2O accounts for approximately 9–10%, 8.5–10.5%, 5.5–7.5% of daily emissions, respectively. It is expected that the findings from this study would be helpful for establishing effective carbon control strategies in the transportation planning stage to reduce road traffic GHG emissions in counties.

Pedestrians’ understanding of fully autonomous vehicles (FAV) intent to stop

2020 ◽  
Vol 64 (1) ◽  
pp. 1931-1932
Author(s):  
Michal Hochman ◽  
Tal Oron-Gilad
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Background Color ◽  
Urban Road ◽  
The Road ◽  
Message Type ◽  
On The Road ◽  
Future Work ◽  
Road Crossing ◽  
Distance Conditions

This study explored pedestrians’ understanding of Fully Autonomous Vehicle (FAV) intention and what influences their decision to cross. Twenty participants saw fixed simulated urban road crossing scenes with a FAV present on the road. The scenes differed from one another in the FAV’s messages: the external Human-Machine Interfaces (e-HMI) background color, message type and modality, the FAV’s distance from the crossing place, and its size. Eye-tracking data and objective measurements were collected. Results revealed that pedestrians looked at the e-HMI before making their decision; however, they did not always make the decision according to the e-HMIs’ color, instructions (in advice messages), or intention (in status messages). Moreover, when they acted according to the e-HMI proposition, for certain distance conditions, they tended to hesitate before making the decision. Findings suggest that pedestrians’ decision making to cross depends on a combination of the e-HMI implementation and the car distance. Future work should explore the robustness of the findings in dynamic and more complex crossing environments.

scholarly journals How virtue signalling makes us better: Moral preferences with respect to autonomous vehicle type choices

2019 ◽  
Author(s):  
Robin Kopecky ◽  
Michaela Košová ◽  
Daniel D. Novotný ◽  
Jaroslav Flegr ◽  
David Černý
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Biological Species ◽  
Transportation Systems ◽  
Personal Choice ◽  
Moral Issues ◽  
Web Based ◽  
The Public ◽  
Vehicle Type ◽  
Research Questions

Autonomous vehicles (henceforth AVs) are expected to significantly benefit our transportation systems, their safety, efficiency, and impact on environment. However, many technical, social, legal, and moral questions and challenges concerning AVs and their introduction to the mass market still remain. One of the pressing moral issues has to do with the choice between AV types that differ in their built-in algorithms for dealing with situations of unavoidable lethal collision. In this paper we present the results of our study of moral preferences with respect to three types of AVs: (1) selfish AVs that protect the lives of passenger(s) over any number of bystanders; (2) altruistic AVs that minimize the number of casualties, even if this leads to death of passenger(s); and (3) conservative AVs that abstain from interfering in such situations even if it leads to the death of a higher number of subjects or death of passenger(s). We furthermore differentiate between scenarios in which participants are to make their decisions privately or publicly, and for themselves or for their offspring. We disregard gender, age, health, biological species and other characteristics of (potential) casualties that can affect the preferences and decisions of respondents in our scenarios. Our study is based on a sample of 2769 mostly Czech volunteers (1799 women, 970 men; age IQR: 25-32). The data come from our web-based questionnaire which was accessible from May 2017 to December 2017. We aim to answer the following two research questions: (1) Whether the public visibility of an AV type choice makes this choice more altruistic and (2) which type of situation is more problematic with regard to the altruistic choice: opting for society as a whole, for oneself, or for one’s offspring.Our results show that respondents exhibit a clear preference for an altruistic utilitarian strategy for AVs. This preference is reinforced if the AV signals its strategy to others. The altruistic preference is strongest when people choose software for everybody else, weaker in personal choice, and weakest when choosing for one’s own child. Based on the results we conclude that, in contrast to a private choice, a public choice is considerably more likely to pressure consumers in their personal choice to accept a non-selfish solution, making it a reasonable and relatively cheap way to shift car owners and users towards higher altruism. Also, a hypothetical voting in Parliament about a single available program is less selfish when the voting does not take place in secret.

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

2019 ◽  
Vol 2673 (9) ◽  
pp. 430-439 ◽  
Author(s):  
Hwapyeong Yu ◽  
Sehyun Tak ◽  
Minju Park ◽  
Hwasoo Yeo
Keyword(s):  
Autonomous Vehicles ◽  
Road Traffic ◽  
Positive Impact ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Market Penetration ◽  
Mixed Traffic ◽  
Negative Effects ◽  
Average Speed ◽  
Time To Collision

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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