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.

Linear Acceleration Car-Following Model Development and Validation

1998 ◽  
Vol 1644 (1) ◽  
pp. 10-19 ◽  
Author(s):  
M.F. Aycin ◽  
R.F. Benekohal
Keyword(s):  
Intelligent Transportation Systems ◽  
Field Data ◽  
Statistical Tests ◽  
Reaction Times ◽  
Linear Acceleration ◽  
Transportation Systems ◽  
Cruise Control ◽  
Vehicle Simulation ◽  
Car Following ◽  
Car Following Model

A linear acceleration car-following model has been developed for realistic simulation of traffic flow in intelligent transportation systems (ITS) applications. The new model provides continuous acceleration profiles instead of the stepwise profiles that are currently used. The brake reaction times of the drivers are simulated effectively and are independent of the simulation time steps. Chain-reaction times of the drivers are also simulated and perception thresholds are incorporated in the model. The preferred time headways are utilized to determine the simulated drivers’ separation during car-following. The features of the model and the realistic vehicle simulation in car-following and in stop-and-go conditions make this model suitable to ITS, especially to autonomous intelligent cruise-control systems. The car-following algorithm is validated at microscopic and macroscopic levels by using field data. Simulated versus field trajectories and statistical tests show very strong agreement between simulation results and field data.

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 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.

scholarly journals A Space-Based Car-Following Model: Development and Application for Managed Motorway System Safety Evaluation

2021 ◽  
Vol 1 (3) ◽  
pp. 443-465
Author(s):  
Kaveh Bevrani ◽  
Edward Chung ◽  
Pauline Teo
Keyword(s):  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Simulation Models ◽  
Transportation Systems ◽  
Speed Limit ◽  
Variable Speed ◽  
Driver Behaviour ◽  
Car Following ◽  
Micro Simulation ◽  
Car Following Model

Traffic safety studies need more than what the current micro-simulation models can provide, as they presume that all drivers exhibit safe behaviors. Therefore, existing micro-simulation models are inadequate to evaluate the safety impacts of managed motorway systems such as Variable Speed Limits. All microscopic traffic simulation packages include a core car-following model. This paper highlights the limitations of the existing car-following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car-following models, modelling driver behaviour with precise parameters such as headways and time-to-collisions. The comparison evaluates the robustness of each car-following model for safety metric reproductions. A new car-following model, based on the personal space concept and fish school model is proposed to simulate more accurate traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit from variable speed limit (VSL) systems. This model can also emulate different traffic states and can be easily calibrated. These research findings facilitate assessing and predicting intelligent transportation systems effects on motorways, using microscopic simulation.

scholarly journals Dynamic Car–Following Model Calibration Using SPSA and ISRES Algorithms

2018 ◽  
Vol 47 (2) ◽  
pp. 146-156 ◽  
Author(s):  
Ioulia Markou ◽  
Vasileia Papathanasopoulou ◽  
Constantinos Antoniou
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Simulation ◽  
Transportation Systems ◽  
Model Parameters ◽  
Time Step ◽  
Car Following ◽  
Calibration Methods ◽  
Wide Range ◽  
Car Following Model ◽  
Parameter Values

Calibration plays a fundamental role in successful applications of traffic simulation and Intelligent Transportation Systems. In this research, the calibration of car–following models is seen as a dynamic problem, which is solved at each individual time–step. The optimization of model parameters is fulfilled using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The output of the optimization is a distribution of parameter values, capturing a wide range of various traffic conditions. The methodology is demonstrated via a case study, where the proposed framework is implemented for the dynamic calibration of the car–following model used in the TransModeler traffic simulation model and Gipps′ model. This method results to model parameter distributions, which are superior to simply using point parameter values, as they are more realistic, capturing the heterogeneity of driver behavior. Flexibility is thus introduced into the calibration process and restrictions generated by conventional calibration methods are relaxed.

scholarly journals Optimization and Evaluation of Platooning Car-Following Models in a Connected Vehicle Environment

2021 ◽  
Vol 13 (6) ◽  
pp. 3474
Author(s):  
Guang Yu ◽  
Shuo Liu ◽  
Qiangqiang Shangguan
Keyword(s):  
Communication Technology ◽  
Intelligent Transportation Systems ◽  
Rapid Development ◽  
Safety Margin ◽  
Practical Method ◽  
Transportation Systems ◽  
Systematic Evaluation ◽  
Connected Vehicle ◽  
Car Following ◽  
Cooperative Driving

With the rapid development of information and communication technology, future intelligent transportation systems will exhibit a trend of cooperative driving of connected vehicles. Platooning is an important application technique for cooperative driving. Herein, optimized car-following models for platoon control based on intervehicle communication technology are proposed. On the basis of existing indicators, a series of evaluation methods for platoon safety, stability, and energy consumption is constructed. Numerical simulations are used to compare the effects of three traditional models and their optimized counterparts on the car-following process. Moreover, the influence of homogenous and heterogeneous attributes on the platoon is analyzed. The optimized model proposed in this paper can improve the stability and safety of vehicle following and reduce the total fuel consumption. The simulation results show that a homogenous platoon can enhance the overall stability of the platoon and that the desired safety margin (DSM) model is better suited for heterogeneous platoon control than the other two models. This paper provides a practical method for the design and systematic evaluation of a platoon control strategy, which is one of the key focuses in the connected and autonomous vehicle industry.

scholarly journals A New Car-Following Model with Consideration of Dynamic Safety Distance

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Tao Wang ◽  
Jing Zhang ◽  
Guangyao Li ◽  
Keyu Xu ◽  
Shubin Li
Keyword(s):  
Traffic Flow ◽  
Velocity Model ◽  
Safe Distance ◽  
Optimal Velocity ◽  
New Model ◽  
Car Following ◽  
Optimal Velocity Model ◽  
Safety Distance ◽  
Car Following Model ◽  
The Impact

In the traditional optimal velocity model, safe distance is usually a constant, which, however, is not representative of actual traffic conditions. This paper attempts to study the impact of dynamic safety distance on vehicular stream through a car-following model. Firstly, a new car-following model is proposed, in which the traditional safety distance is replaced by a dynamic term. Then, the phase diagram in the headway, speed, and sensitivity spaces is given to illustrate the impact of a variable safe distance on traffic flow. Finally, numerical methods are conducted to examine the performance of the proposed model with regard to two aspects: compared with the optimal velocity model, the new model can suppress traffic congestion effectively and, for different safety distances, the dynamic safety distance can improve the stability of vehicular stream. Simulation results suggest that the new model is able to enhance traffic flow stability.

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