Macroscopic Effects of Multianticipative Driving Behavior on Traffic Flow Characteristics

2009 ◽  
Vol 2124 (1) ◽  
pp. 103-112 ◽  
Author(s):  
D. Ngoduy
Keyword(s):  
Traffic Flow ◽  
Flow Characteristics ◽  
Driving Behavior ◽  
Traffic Flow Characteristics

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

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.

Research on traffic flow characteristics at signal intersection

2017 ◽  
Vol 31 (26) ◽  
pp. 1750238 ◽  
Author(s):  
Jun-Wei Zeng ◽  
Sen-Bin Yu ◽  
Yong-Sheng Qian ◽  
Xu-Ting Wei ◽  
Xiao Feng ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Flow Characteristics ◽  
Driving Behavior ◽  
Signalized Intersection ◽  
Flow Density ◽  
Two Dimensional ◽  
Cellular Automata Model ◽  
Left Turn ◽  
Traffic Flow Characteristics ◽  
Research Objects

Based on the cautious driving behavior and the principle of the vehicles at left-side having priority to pass in the intersection, a two-dimensional cellular automata model for planar signalized intersection (NS-STCA) is established. The different turning vehicles are regarded as the research objects and the effect of the left-turn probability, signal cycle, vehicle flow density on traffic flow at the intersection is investigated.

scholarly journals Assessing the Influence of Adverse Weather on Traffic Flow Characteristics Using a Driving Simulator and VISSIM

2019 ◽  
Vol 11 (3) ◽  
pp. 830 ◽  
Author(s):  
Chen Chen ◽  
Xiaohua Zhao ◽  
Hao Liu ◽  
Guichao Ren ◽  
Yunlong Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Driving Simulator ◽  
Traffic Simulation ◽  
Flow Characteristics ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Point Of View ◽  
Microscopic Traffic Simulation ◽  
Adverse Weather ◽  
Traffic Flow Characteristics

The occurrence of adverse weather exacerbates traffic flow conditions, often leading to severe traffic congestions. Many studies have been conducted based on field-collected data to obtain the effects of weather on traffic flow characteristics. However, there is a limitation for filed data-based studies, in that weather conditions and traffic conditions are both noncontrollable and nonrepeatable, making it difficult to comprehensively assess the influence of weather conditions, especially the rare extreme weather conditions, on traffic flow characteristics. This paper proposes to assess these effects with the combination of driving simulator and traffic simulation. A driving simulator can collect driving behavior by conducting weather-related driving simulation experiments, while a microscopic traffic simulation program can evaluate the changes in traffic flow characteristics by inputting driving behavior parameters coming from the driving simulator. The proposed method can overcome the limitation of the field data-based approach. In this paper, the structure of the assessment platform is introduced at first. Then a verification experiment is conducted to measure the influences of adverse weather conditions on traffic flow characteristics. The verification experiment results show that the influences of adverse weather on traffic flow characteristics have consistent tendencies with outcomes from previous research and demonstrate that the method is practicable for the analysis of the influence of weather on traffic flow characteristics. This paper provides a practical way to analyze the influence of weather on traffic flow from driving behavior’s point of view.

scholarly journals Semi-Continuous Spatial Statistical Analysis Using AIS Data for Vessel Traffic Flow Characteristics in Fairway

2021 ◽  
Vol 9 (4) ◽  
pp. 378
Author(s):  
Jong Kwan Kim
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Automatic Identification ◽  
Identification System ◽  
Spatial Statistical Analysis ◽  
Traffic Characteristics ◽  
Traffic Distribution ◽  
Traffic Flow Characteristics ◽  
Marine Accidents

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.

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