scholarly journals Calibrating the Wiedemann 99 Car-Following Model for Bicycle Traffic

2021 ◽  
Vol 13 (6) ◽  
pp. 3487
Author(s):  
Heather Kaths ◽  
Andreas Keler ◽  
Klaus Bogenberger
Keyword(s):  
Motor Vehicle ◽  
Trajectory Data ◽  
Traffic Light ◽  
Microscopic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Car Following Model ◽  
Behavior Models ◽  
Traffic Simulations ◽  
Observed Behavior

Car-following models are used in microscopic simulation tools to calculate the longitudinal acceleration of a vehicle based on the speed and position of a leading vehicle in the same lane. Bicycle traffic is usually included in microscopic traffic simulations by adjusting and calibrating behavior models developed for motor vehicle traffic. However, very little work has been carried out to examine the following behavior of bicyclists, calibrate following models to fit this observed behavior, and determine the validity of these calibrated models. In this paper, microscopic trajectory data collected in a bicycle simulator study are used to estimate the following parameters of the psycho-physical Wiedemann 99 car-following model implemented in PTV Vissim. The Wiedemann 99 model is selected due to the larger number of assessable parameters and the greater possibility to calibrate the model to fit observed behavior. The calibrated model is validated using the indicator average queue dissipation time at a traffic light on the facilities ranging in width between 1.5 m to 2.5 m. Results show that the parameter set derived from the microscopic trajectory data creates more realistic simulated bicycle traffic than a suggested parameter set. However, it was not possible to achieve the large variation in average queue dissipation times that was observed in the field with either of the tested parameter sets.

scholarly journals Analyzing the behavior of bicyclists using a bicycle simulator with a coupled SUMO and DYNA4 simulated environment

10.29007/dcmp ◽  
2019 ◽  
Author(s):  
Heather Kaths ◽  
Andreas Keler ◽  
Jakob Kaths ◽  
Fritz Busch
Keyword(s):  
Traffic Control ◽  
Simulation Software ◽  
Control Measures ◽  
Signalized Intersection ◽  
Trajectory Data ◽  
Road Users ◽  
Simulated Environment ◽  
Simulator Study ◽  
Behavior Models ◽  
Traffic Simulations

Operational behavior models are used in traffic simulations to represent the subconscious, short-termdecisions made by road users to respond to other road users, the infrastructure and traffic control measures. Calibration and validation of these models can be achieved using observed trajectory data from real road users. For lane bound traffic, it is assumed that road users intend to follow a given lane with a certain desired speed across the intersection. Any deviation from this planned path is in response to other road users or the environment. It is difficult, however, to identify and separate the desired movement of more flexible road users that do not follow lane disciple, such as bicyclists, from movements made as a reaction to other road users or obstacles. This can lead to poor calibration of operational behavior models and unrealistic behavior in the simulation. Tactical behavior models recreate the conscious decisions made on a time horizon of seconds to minutes to cope with the immediate traffic situation. As such, tactical behavior models are responsible for selecting the planned path across an intersection.Here, SUMO is coupled with the simulation software DYNA4 to create a simulated road environment for a bicycle simulator. Trajectories observed in reality are displayed as potential prescribed pathways across the simulated intersection. Participants in the simulator study are instructed to select and follow one of the prescribed pathways as closely as possible while responding naturally to other road users and obstacles in the environment. The resulting trajectory data is used to calibrate existing operation al and tactical path finding behavior models for bicyclists at signalized intersection.

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 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.

scholarly journals Calibration of the Gipps Car-following Model Using Trajectory Data

2014 ◽  
Vol 3 ◽  
pp. 952-961 ◽  
Author(s):  
Luís Vasconcelos ◽  
Luís Neto ◽  
Sílvia Santos ◽  
Ana Bastos Silva ◽  
Álvaro Seco
Keyword(s):  
Trajectory Data ◽  
Car Following ◽  
Car Following Model

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.

Sign in / Sign up

Export Citation Format

Share Document