scholarly journals A Support Vector Regression Approach for Investigating Multianticipative Driving Behavior

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Lu ◽  
Shaoquan Ni ◽  
Scott S. Washburn
Keyword(s):  
Support Vector Regression ◽  
Driving Behavior ◽  
Support Vector ◽  
Trajectory Data ◽  
Car Following ◽  
Congested Traffic ◽  
Car Following Model ◽  
Importance Analysis ◽  
Model Training ◽  
Vehicle Trajectory

This paper presents a Support Vector Regression (SVR) approach that can be applied to predict the multianticipative driving behavior using vehicle trajectory data. Building upon the SVR approach, a multianticipative car-following model is developed and enhanced in learning speed and predication accuracy. The model training and validation are conducted by using the field trajectory data extracted from the Next Generation Simulation (NGSIM) project. During the model training and validation tests, the estimation results show that the SVR model performs as well as IDM model with respect to the model prediction accuracy. In addition, this paper performs a relative importance analysis to quantify the multianticipation in terms of the different stimuli to which drivers react in platoon car following. The analysis results confirm that drivers respond to the behavior of not only the immediate leading vehicle in front but also the second, third, and even fourth leading vehicles. Specifically, in congested traffic conditions, drivers are observed to be more sensitive to the relative speed than to the gap. These findings provide insight into multianticipative driving behavior and illustrate the necessity of taking into account multianticipative car-following model in microscopic traffic simulation.

Car-Following Model Calibration and Analysis of Intra-Driver Heterogeneity

2010 ◽  
Vol 108-111 ◽  
pp. 805-810 ◽  
Author(s):  
Hao Wang ◽  
Wei Wang ◽  
Jun Chen
Keyword(s):  
Model Calibration ◽  
Optimization Method ◽  
Acceleration Process ◽  
Trajectory Data ◽  
Car Following ◽  
Car Following Model ◽  
Vehicle Trajectory ◽  
Best Fit ◽  
Insight Into ◽  
Selection Of

This paper presents a methodology for car-following models calibration with vehicle trajectory data. A two-step optimization method is performed for searching the best-fit parameters of two popular car-following models, namely, the Helly model and the IDM model. The model calibration results verify the validity of the optimization method. Based on the results of calibrations, the intra-driver heterogeneity of driving behavior between the acceleration process and the deceleration process is studied. It is found that obvious intra-driver heterogeneities exist in driving behaviours between acceleration processes and deceleration processes of car-following. Besides, some criteria are proposed for the selection of sub-trajectories corresponding to both the acceleration and the deceleration processes of car-following. This work not only develops a general approach for car-following model calibration with vehicle trajectory data, but also provides insight into the intra-driver heterogeneity in car-following behaviours.

scholarly journals Evaluation of the Gradient Boosting of Regression Trees Method on Estimating Car-Following Behavior

2018 ◽  
Vol 2672 (45) ◽  
pp. 136-146 ◽  
Author(s):  
Sina Dabiri ◽  
Montasir Abbas
Keyword(s):  
Regression Tree ◽  
Gradient Boosting ◽  
Data Sets ◽  
Trajectory Data ◽  
Car Following ◽  
Complex Interactions ◽  
Car Following Model ◽  
Mathematical Formulas ◽  
Vehicle Trajectory ◽  
Motion Characteristics

Car-following models, as the essential part of traffic microscopic simulations, have been utilized to analyze and estimate longitudinal drivers’ behavior for sixty years. The conventional car-following models use mathematical formulas to replicate human behavior in car-following phenomenon. The incapability of these approaches to capture the complex interactions between vehicles calls for deploying advanced learning frameworks to consider more detailed behavior of drivers. In this study, we apply the gradient boosting of regression tree (GBRT) algorithm to vehicle trajectory data sets, which have been collected through the Next Generation Simulation (NGSIM) program, to develop a new car-following model. First, the regularization parameters of the proposed method are tuned using cross-validation technique and sensitivity analysis. Second, prediction performance of the GBRT is compared to the world-famous Gazis-Herman-Rothery (GHR) model, when both models have been trained on the same data sets. The estimation results of the models on unseen records indicate the superiority of the GBRT algorithm in capturing the motion characteristics of two successive vehicles.

scholarly journals An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment

2020 ◽  
Vol 12 (4) ◽  
pp. 1552 ◽  
Author(s):  
Shuaiyang Jiao ◽  
Shengrui Zhang ◽  
Bei Zhou ◽  
Zixuan Zhang ◽  
Liyuan Xue
Keyword(s):  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Information ◽  
Trajectory Data ◽  
Optimal Velocity ◽  
Car Following ◽  
V2v Communication ◽  
Communication Environment ◽  
Car Following Model ◽  
Vehicle Trajectory

In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers’ characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers’ characteristics and the following car’s optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers’ characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers’ characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles’ mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers’ characteristics into account under the V2V communication situation for car-following theory.

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.

An extended heterogeneous car-following model accounting for anticipation driving behavior and mixed maximum speeds

2018 ◽  
Vol 382 (7) ◽  
pp. 489-498 ◽  
Author(s):  
Fengxin Sun ◽  
Jufeng Wang ◽  
Rongjun Cheng ◽  
Hongxia Ge
Keyword(s):  
Driving Behavior ◽  
Car Following ◽  
Car Following Model

scholarly journals Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kai Huang ◽  
Ming-Yi You ◽  
Yun-Xia Ye ◽  
Bin Jiang ◽  
An-Nan Lu
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Direction Of Arrival ◽  
Direction Finding ◽  
Training Data ◽  
Data Driven ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Model Training ◽  
Finding Method

The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved.

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