Can Results of car-following Model Calibration Based on Trajectory Data be Trusted?

2012 ◽  
Vol 2315 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Vincenzo Punzo ◽  
Biagio Ciuffo ◽  
Marcello Montanino
Keyword(s):  
Model Calibration ◽  
Trajectory Data ◽  
Car Following ◽  
Car Following Model

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 Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5539 ◽  
Author(s):  
Mădălin-Dorin Pop ◽  
Octavian Proștean ◽  
Tudor-Mihai David ◽  
Gabriela Proștean
Keyword(s):  
Fuzzy Inference System ◽  
Model Calibration ◽  
Fuzzy Inference ◽  
Real Data ◽  
Traffic Model ◽  
Model Parameters ◽  
Inference System ◽  
Car Following ◽  
Car Following Model ◽  
Takagi Sugeno

Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.

scholarly journals DeepCF: A Deep Feature Learning-Based Car-Following Model Using Online Ride-Hailing Trajectory Data

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yizhen Xie ◽  
Qichao Ni ◽  
Osama Alfarraj ◽  
Haoran Gao ◽  
Guojiang Shen ◽  
...  
Keyword(s):  
Reaction Time ◽  
Feature Learning ◽  
Generative Adversarial Networks ◽  
Trajectory Data ◽  
Time Model ◽  
Car Following ◽  
Deep Feature ◽  
Car Following Model ◽  
Deep Feature Learning ◽  
Fatigue Driving

The car-following model describes the microscopic behavior of the vehicle. However, the existing car-following models set the drivers’ reaction time to a fixed value without considering its dynamics. In order to improve the accuracy of car-following model, this paper proposes Deep Feature Learning-based Car-Following Model (DeepCF), a car-following model based on fatigue driving and Generative Adversarial Networks (GAN). The model is composed of the drivers’ reaction time model and the car-following decision algorithm. First, we regard driving fatigue as the starting point to study the influence of driving time and the acceleration of the preceding vehicle on the drivers’ reaction time, and develop a coarse-grained drivers’ reaction time model. Secondly, considering the impact of fatigue driving on car-following decisions, we utilize GAN to generate a driving decision database based on reaction time and use Euclidean distance as a decision search indicator. Finally, we conduct experiments on a real data set, and the results indicate that our DeepCF model is superior to baseline models.

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 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.

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