Traffic lane-changing modeling and scheduling with game theoretical strategy

2021 ◽  
Author(s):  
Jian Guo ◽  
Istvan Harmati
Keyword(s):  
Lane Changing ◽  
Traffic Lane

scholarly journals Traffic Characteristics for A Freeway Normal Section at Baghdad City

2018 ◽  
Vol 7 (4.20) ◽  
pp. 283
Author(s):  
Jalal T. S. Al-Obaedi ◽  
Muhanad Al-temimy ◽  
Amal Ali
Keyword(s):  
Traffic Flow ◽  
Research Work ◽  
Distribution Data ◽  
Lane Changing ◽  
Traffic Characteristics ◽  
Traffic Lane ◽  
Video Recordings ◽  
Negative Exponential Distribution ◽  
Headway Distribution ◽  
Negative Exponential

Traffic characteristics at highway sections are usually varying based on many factors including type of highways, geometric design and drivers’ behavior at a given area (country).  This paper focuses on finding the characteristics for traffic on selected normal freeway section at Baghdad city.  Video recordings and speed gun are used to collect data from a basic freeway section within Mohammed Al-Qassim freeway that represents the busiest freeway at the city.  The estimated characteristics include the distribution of traffic among the available lanes, desired speed of traffic, lane-changing frequency, and headway distribution.  For traffic distribution, it is found that traffic concentrates more in off side lane compared with other lanes for moderate to high flow rates.  Regression models have been developed based on the available lane distribution data.  The lane found to be increased with the increasing of traffic flow and the desired speeds found to be normally distributed.  Examining the headway data shows that the shifted negative exponential distribution can be used to represent the headway distribution for low to intermediate traffic flow only.  The findings of this work provides a good database for traffic characteristics for Iraqi highways as little effort has been given in previous research work.  

scholarly journals Study of driver behaviour at turbo-roundabouts

2016 ◽  
Vol 38 (2) ◽  
pp. 17-28 ◽  
Author(s):  
Janusz Chodur ◽  
Radosław Bąk
Keyword(s):  
Prediction Models ◽  
Driver Behaviour ◽  
Lane Changing ◽  
Physical Separation ◽  
Crash Prediction ◽  
Traffic Lane ◽  
Traffic Corridor ◽  
The Subject ◽  
Crash Prediction Models ◽  
Traffic Rules

The article presents the results of preliminary research into the behaviour of drivers at turbo-roundabouts. The subject of the research included the frequency of driver behaviour against the traffic rules, and the speed at which vehicles drive through turbo-roundabouts. One of the crucial problems which was analysed was the influence of different kinds of traffic lane division on the behaviour of drivers. The analysis results affirm that the raised lane dividers can visibly improve the propensity of drivers to stay within the designated traffic corridor. However, it does not eliminate the phenomenon of improper lane changing on circulatory roadway. The physical separation of traffic lanes has not been determined to introduce any additional hazard. The speed of vehicles encroaching upon the neighbouring traffic corridor is visibly higher than this of vehicles following traffic rules. Using crash prediction models developed for single- and multi-lane roundabouts, the authors of the research estimated that lane dividers may reduce the number of crashes from about 10% to 17%.

scholarly journals Measuring traffic lane‐changing by converting video into space–time still images

2019 ◽  
Vol 34 (6) ◽  
pp. 488-505 ◽  
Author(s):  
Marcel Sala ◽  
Francesc Soriguera ◽  
Kevin Huillca ◽  
Verónica Vilaplana
Keyword(s):  
Space Time ◽  
Still Images ◽  
Lane Changing ◽  
Traffic Lane

Study on the Mode of Lane Changing for Turning in Urban Road

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Shiqiang Cheng ◽  
Liyang Wei ◽  
Xuelan Ma ◽  
Jianfeng Shen ◽  
Jian Wang
Keyword(s):  
Lane Changing ◽  
Urban Road

A Review on Lane Changing for Intelligent Vehicle

2015 ◽  
Vol 8 (3) ◽  
pp. 184-194 ◽  
Author(s):  
Ronghui Zhang ◽  
Fuliang Li ◽  
Xuecai Yu ◽  
Zhonghua Zhang ◽  
Feng You ◽  
...  
Keyword(s):  
Intelligent Vehicle ◽  
Lane Changing

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.

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