scholarly journals Defining Highway Node Acceptance Capacity (HNAC): Theoretical Analysis and Data Simulation

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xingliang Liu ◽  
Jinliang Xu ◽  
Yaping Dong ◽  
Han Ru ◽  
Zhihao Duan
Keyword(s):  
Propagation Mechanism ◽  
Data Simulation ◽  
Explicit Equation ◽  
Lane Changing ◽  
Car Following ◽  
Traffic Flow Theory ◽  
Background Traffic ◽  
Highway Network ◽  
Traffic Oscillations ◽  
The Relationship

A new concept of Highway Node Acceptance Capacity (HNAC) is proposed in this paper inspired by a field data observation. To understand HNAC in microscopic view, boundary condition of successful merging is found using car-following behaviours and lane-changing rules, which could also explain traffic oscillations. In macroscopic view, linear positive relationship between HNAC and background traffic volume is obtained based on moving bottleneck. To determine the explicit form of the relationship, data simulation considering car-following behaviours and traffic flow theory is used. In the results, the synchronization phenomenon of oscillation in on-ramp (with respect to main road) and intersected road is found. The explicit equation of HNAC is determined based on standard deviation and correlation coefficient analysis, and also proved to be accurate with model validation, which is helpful in studies related to propagation mechanism of traffic emergencies on highway network.

Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

2021 ◽  
pp. 036119812199938
Author(s):  
Li Zhao ◽  
Laurence Rilett ◽  
Mm Shakiul Haque
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Vehicle Body ◽  
Lane Change ◽  
Lane Changing ◽  
Car Following ◽  
Pilot Model ◽  
Naturalistic Driving

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

scholarly journals Computer Digital Technology-Based Educational Platform for Protection and Activation Design of Ming Furniture

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhe Peng ◽  
Yichen Du
Keyword(s):  
Production Management ◽  
Educational Software ◽  
Training Design ◽  
Data Simulation ◽  
Technical Management ◽  
Design Program ◽  
Training Plan ◽  
The World ◽  
Operation Platform ◽  
The Relationship

Chinese Ming-style furniture is the first of the three major furnitures in the world, which has high artistic value and complete structural system. The protection of Ming-style furniture and making its design show vitality that has always been a research topic in China and even in the world. Based on 3D virtual simulation technology, this paper uses 3D scanning reverse data acquisition technology and intelligent operation of computer engine to realize big data simulation and develops the design software of Ming furniture. By means of computer information technology, we interpret and present the structural thinking and design concept of Ming furniture and transform it into design program software. This has formed a system-wide educational software operation platform for knowledge reserve, thinking training, design and application, and achievement transformation. By applying the mechanism strategy of production and teaching to the talent training plan of colleges and universities, the underlying logic of talent training, technical management, and production management that can open up the innovative industry of Ming furniture is also constructed. After experimenting with the platform software, students are able to understand the relationship between the structure and form of Ming furniture and can design new styles that fit the logic of Ming furniture shapes as they prefer.

Minimum Safety Distances for Emergency Braking Maneuvers in Car-Following Applications

2020 ◽  
Author(s):  
Devin Schafer ◽  
Pingen Chen
Keyword(s):  
Aerodynamic Force ◽  
Lane Change ◽  
Passenger Vehicle ◽  
Lane Changing ◽  
Car Following ◽  
Emergency Braking ◽  
Safety Distance ◽  
Bicycle Model ◽  
Vehicle Efficiency ◽  
Different Types

Abstract Platooning/car following has been considered as a promising approach for improving vehicle efficiency due to the reduction of aerodynamic force when closely following a pilot vehicle. However, safety is a major concern in the close car platooning/following. This paper investigates the minimum inter-vehicle distances required for a passenger vehicle to safely travel behind a heavy-duty truck with three different types of emergency maneuvers. The three emergency maneuvers considered are braking only, steering only, and braking then steering, where steering refers to a single lane change maneuver. Numerical analysis is conducted for deriving the clearance space in the braking only scenario. In addition, simulations are conducted in MATLAB/Simulink, using a bicycle model for the vehicle dynamics, to examine the minimum safe following distance for the other two scenarios. The simulation results show that, for initial vehicle speeds greater than 8 m/s, a lane change maneuver requires the shortest safety distance. Braking followed by lane changing usually requires the largest minimum safety distance.

Development of Operation Guidelines for Leader–Follower Autonomous Maintenance Vehicles at Work Zone Locations

2021 ◽  
pp. 036119812110566
Author(s):  
Qing Tang ◽  
Xianbiao Hu ◽  
Ruwen Qin
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Field Testing ◽  
Work Zone ◽  
Lane Changing ◽  
The Public ◽  
Car Following ◽  
Technical Requirements ◽  
Car Following Model ◽  
Niche Area

The rapid advancement of connected and autonomous vehicle (CAV) technologies, although possibly years away from wide application to the general public travel, are receiving attention from many state Departments of Transportation (DOT) in the niche area of using autonomous maintenance technology (AMT) to reduce fatalities of DOT workers in work zone locations. Although promising results are shown in testing and deployments in several states, current autonomous truck mounted attenuator (ATMA) system operators are not provided with much practical driving guidance on how to drive these new vehicle systems in a way that is safe to both the public and themselves. To this end, this manuscript aims to model and develop a set of rules and instructions for ATMA system operators, particularly when it comes to critical locations where essential decision making is needed. Specifically, three technical requirements are investigated: car-following distance, critical lane-changing gap distance, and intersection clearance time. Newell’s simplified car-following model, and the classic lane-changing behavior model are modified, with roll-ahead distance taken into account, to model the driving behaviors of the ATMA vehicles at those critical decision-making locations. Data are collected from real-world field testing to calibrate and validate the developed models. The modeling outputs suggest important thresholds for ATMA system operators to follow. For example, on a freeway with a speed limit of 70 mph and ATMA operating speed of 10 mph, car-following distance should be no less than 75 ft for the lead truck and 100 ft for the follower truck, the critical lane-changing gap distance is 912 ft, and a minimum intersection clearance is 15 s, which are all much higher than the requirements for a general vehicle.

Modeling Speed Disturbance Absorption following Current State–Control Action–Expected State Chains

2005 ◽  
Vol 1934 (1) ◽  
pp. 83-93 ◽  
Author(s):  
Ruihua Tao ◽  
Heng Wei ◽  
Yinhai Wang ◽  
Virginia P. Sisiopiku
Keyword(s):  
Recovery Period ◽  
Control Action ◽  
State Control ◽  
Trajectory Data ◽  
Lane Changing ◽  
Car Following ◽  
Current State ◽  
Speed Advantage ◽  
Vehicle Trajectory ◽  
A Current

This paper explores driver behavior in a paired car-following mode in response to a speed disturbance from a front vehicle. A current state– control action–expected state (SAS) chain is developed to provide a framework for modeling of the hierarchy of expected actions incurred during the need for speed disturbance absorption. Three car-following scenarios and one lane-changing scenario are identified with defined perceptual informative variables to describe the process of speed disturbance absorption. Those variables include dynamic spacing versus the follower's speed, disturbance-effecting and -ending spacing, headway, acceleration– deceleration, speed recovery period, speed advantage, and lane-changing duration. A significant improvement in car-following modeling introduced in the paper is the integration of car-following and lane-changing behaviors in the SAS chain. Moreover, critical values of perceptual informative variables are statistically developed as a function of the follower's speed by using observed vehicle trajectory data. Furthermore, models that determine the probability of a lane change in response to a speed disturbance and models for acceptable lane-changing decision-making conditions at the adjacent lanes are developed on the basis of the analysis of observed vehicle trajectory data. The work presented in this paper provides an analysis of speed disturbance and speed absorption phenomena and car-following and lane-changing behaviors at the microscopic level. This work establishes the foundation for further research on multiple speed disturbance absorption and its impact on traffic stabilities at the macroscopic analysis level.

scholarly journals Pattern Analysis of Driver’s “Pressure-State-Response” in Traffic Congestion

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Weiwei Qi ◽  
Yulong Pei ◽  
Mo Song ◽  
Yiming Bie
Keyword(s):  
Traffic Congestion ◽  
Logical Structure ◽  
Congestion Management ◽  
Lane Changing ◽  
State Response ◽  
Traffic Stream ◽  
And Behavior ◽  
The Relationship ◽  
Systematic Framework ◽  
Pressure State

Traffic congestion, which has a direct impact on the driver’s mood and action, has become a serious problem in rush hours in most cities of China. Currently, the study about driver’s mood and action in traffic congestion is scarce, so it is necessary to work on the relationship among driver’s mood and action and traffic congestion. And the PSR (pressure-state-response) framework is established to describe that relationship. Here, PSR framework is composed of a three-level logical structure, which is composed of traffic congestion environment, drivers’ physiology change, and drivers’ behavior change. Based on the PSR framework, various styles of drivers have been chosen to drive on the congested roads, and then traffic stream state, drivers’ physiology, and behavior characters have been measured via the appropriative equipment. Further, driver’s visual characteristics and lane changing characteristics are analyzed to determine the parameters of PSR framework. According to the PSR framework, the changing law of drivers’ characteristics in traffic congestion has been obtained to offer necessary logical space and systematic framework for traffic congestion management.

scholarly journals Multi-lane Changing Model with Coupling Driving Intention and Inclination

2017 ◽  
Vol 29 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Jiangfeng Wang ◽  
Chang Gao ◽  
Zhouyuan Zhu ◽  
Xuedong Yan
Keyword(s):  
Risk Factor ◽  
Lane Changing ◽  
Traffic Demand ◽  
Average Speed ◽  
Slowing Down ◽  
Behavioural Characteristics ◽  
Proposed Model ◽  
Density Flow ◽  
The Impact ◽  
The Relationship

Considering the impact of drivers’ psychology and behaviour, a multi-lane changing model coupling driving intention and inclination is proposed by introducing two quantitative indices of intention: strength of lane changing and risk factor. According to the psychological and behavioural characteristics of aggressive drivers and conservative drivers, the safety conditions for lane changing are designed respectively. The numerical simulations show that the proposed model is suitable for describing the traffic flow with frequent lane changing, which is more consistent with the driving behaviour of drivers in China. Compared with symmetric two-lane cellular automata (STCA) model, the proposed model can improve the average speed of vehicles by 1.04% under different traffic demands when aggressive drivers are in a higher proportion (the threshold of risk factor is 0.4). When the risk factor increases, the average speed shows the polarization phenomenon with the average speed slowing down in big traffic demand. The proposed model can reflect the relationship among density, flow, and speed, and the risk factor has a significant impact on density and flow.

scholarly journals Mobile Phone Use in a Car-Following Situation: Impact on Time Headway and Effectiveness of Driver’s Rear-End Risk Compensation Behavior via a Driving Simulator Study

2020 ◽  
Vol 17 (4) ◽  
pp. 1328 ◽  
Author(s):  
Yunxing Chen ◽  
Rui Fu ◽  
Qingjin Xu ◽  
Wei Yuan
Keyword(s):  
Regression Model ◽  
Mobile Phone ◽  
Driving Simulator ◽  
Accident Risk ◽  
Mobile Phone Use ◽  
Car Following ◽  
Time Headway ◽  
Accident Probability ◽  
Compensation Behavior ◽  
The Relationship

Mobile phone use while driving has become one of the leading causes of traffic accidents and poses a significant threat to public health. This study investigated the impact of speech-based texting and handheld texting (two difficulty levels in each task) on car-following performance in terms of time headway and collision avoidance capability; and further examined the relationship between time headway increase strategy and the corresponding accident frequency. Fifty-three participants completed the car-following experiment in a driving simulator. A Generalized Estimating Equation method was applied to develop the linear regression model for time headway and the binary logistic regression model for accident probability. The results of the model for time headway indicated that drivers adopted compensation behavior to offset the increased workload by increasing their time headway by 0.41 and 0.59 s while conducting speech-based texting and handheld texting, respectively. The model results for the rear-end accident probability showed that the accident probability increased by 2.34 and 3.56 times, respectively, during the use of speech-based texting and handheld texting tasks. Additionally, the greater the deceleration of the lead vehicle, the higher the probability of a rear-end accident. Further, the relationship between time headway increase patterns and the corresponding accident frequencies showed that all drivers’ compensation behaviors were different, and only a few drivers increased their time headway by 60% or more, which could completely offset the increased accident risk associated with mobile phone distraction. The findings provide a theoretical reference for the formulation of traffic regulations related to mobile phone use, driver safety education programs, and road safety public awareness campaigns. Moreover, the developed accident risk models may contribute to the development of a driving safety warning system.

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