Calibrating Wiedemann-99 Model Parameters to Trajectory Data of Mixed Vehicular Traffic

2021 ◽  
pp. 036119812110375
Author(s):  
Ankit Anil Chaudhari ◽  
Karthik K. Srinivasan ◽  
Bhargava Rama Chilukuri ◽  
Martin Treiber ◽  
Ostap Okhrin
Keyword(s):  
Goodness Of Fit ◽  
Simulation Models ◽  
Microscopic Level ◽  
Model Parameters ◽  
Mixed Traffic ◽  
Relative Speed ◽  
Trajectory Data ◽  
Conventional Vehicle ◽  
Car Following ◽  
Emergency Braking

We propose a new methodology for calibrating Wiedemann-99 vehicle-following parameters for mixed traffic (different conventional vehicle classes) based on trajectory data. The existing acceleration equations of the Wiedemann model are modified to represent more realistic driving behavior. Exploratory analysis of simulation data revealed that different Wiedemann-99 model parameters could lead to similar macroscopic behavior, highlighting the importance of calibration at the microscopic level. Therefore, the proposed methodology is based on optimizing performance measures at the microscopic level (acceleration, speed, and trajectory profiles) to estimate suitable calibration parameters. Further, the goodness of fit for the observed data is sensitive to the numerical integration method used to compute vehicles’ velocity and position. We found that the calibrated parameters using the proposed methodology perform better than other approaches for calibrating mixed traffic. The results reveal that the calibrated parameter values and, consequently, the thresholds that delineate closing, following, emergency braking, and opening regimes, vary between two-wheelers and cars. The window (in the relative speed versus gap plot) for the unconscious following is larger for cars while the free-flow regime is more extensive for two-wheelers. Moreover, under the same relative speed and gap stimulus, two-wheelers and cars may be in different regimes and display different acceleration responses. Thus, accurate calibration of each vehicle’s parameters is essential for developing micro-simulation models for mixed traffic. The calibration analysis results of strict and overlapping staggered car following signify an impact of staggered car following compared with strict car following which demands separate calibration for strict and staggered following.

Analysis of Vehicle-Following Behavior in Mixed Traffic Conditions using Vehicle Trajectory Data

2020 ◽  
Vol 2674 (11) ◽  
pp. 842-855
Author(s):  
Madhuri Kashyap N. R. ◽  
Bhargava Rama Chilukuri ◽  
Karthik K. Srinivasan ◽  
Gowri Asaithambi
Keyword(s):  
Mixed Traffic ◽  
Relative Speed ◽  
Linear Regression Models ◽  
Trajectory Data ◽  
Flow Models ◽  
Traffic Conditions ◽  
Traffic Flow Models ◽  
Vehicle Trajectory ◽  
New Variables ◽  
The Relationship

In mixed traffic streams without lane discipline, driving behaviors are complex and difficult to model. However, limited attempts have been made to study the characteristics of these maneuvers using trajectory data. This paper proposes a novel use of vehicle trajectory data to identify car–car and auto–car pairs in the following regime and the regime duration, classify pairs as strict and staggered following, and investigate the factors influencing the following vehicle’s speed under different regimes in mixed traffic. Oblique trajectories and relative speed hysteresis plots are used to identify vehicle pairs in the steady-state following regime. Two new variables, oblique spacing (R) and the angle between the leader and the follower (θ), are proposed. Multiple linear regression models for the follower speed in strict and staggered following regimes are developed. The results show that cars exhibit following behavior more often than other vehicles. Also, while car–car pairs display both left and right staggered following, auto–car pairs predominantly demonstrate left staggered following. Regression analysis shows that the relationship between R and the speed of the following vehicle differs significantly when θ is close to 90° than when it deviates from 90°. The speed of followers is affected by leader and relative speeds. However, the relative speed has a smaller influence in both right and left staggered cases than strict follower cases. Finally, this study provides empirical evidence of qualitative and quantitative differences among following behaviors that can help in developing better microscopic traffic flow models for mixed traffic conditions.

Operational safety of automated and human driving in mixed traffic environments: A perspective of car-following behavior

2021 ◽  
pp. 1748006X2110506
Author(s):  
Tao Li ◽  
Xu Han ◽  
Jiaqi Ma ◽  
Marilia Ramos ◽  
Changju Lee
Keyword(s):  
Traffic Safety ◽  
Risk Mitigation ◽  
Safety Evaluation ◽  
Mixed Traffic ◽  
Trajectory Data ◽  
Road Transportation ◽  
Baseline Model ◽  
Car Following ◽  
On The Road ◽  
The Impact

The advent of automated vehicles (AVs) will provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicle (HV) to a fully AV traffic environment, there will be a mixed traffic flow including both HVs and AVs. The impact of introducing AVs into existing traffic, however, has not yet been fully understood. In this paper, we advance this understanding by conducting mixed traffic safety evaluation from the perspective of car-following behavior using real-world AV operational data of mixed traffic. To understand how the AVs impact other vehicles on the road, we analyzed the operational behaviors of HV-following-HV, AV-following-HV, and HV-following-AV. A selected car-following model is calibrated, and results show that there are significant differences between the HV-following-HV and the other two groups, indicating safe AV behavior and changes in HV behavior (i.e. less aggressive, safer) after the introduction of AVs into the traffic. Additionally, to understand AV behavioral safety, we investigate behavior predictions (one of the most critical inputs for AVs to make car-following decisions) of AVs and their surrounding vehicles using a mature baseline model and a new Conditional Variational Autoencoder (CVAE) framework. The result shows potential risks of inaccurate predictions of the baseline model and the necessity to consider additional factors, such as vehicle interactions and driver behavior, into the prediction for risk mitigation. Arterial vehicle trajectory data from the Lyft Level 5 Dataset is applied to test the proposed methodological framework to understand the car-following safety risks of HVs and AVs in the mixed traffic stream.

scholarly journals Calibration of Micro-Simulation Model Parameters for Heterogeneous Traffic using Mode-Specific Performance Measure

2020 ◽  
Vol 2674 (1) ◽  
pp. 135-147 ◽  
Author(s):  
Kinjal Bhattacharyya ◽  
Bhargab Maitra ◽  
Manfred Boltze
Keyword(s):  
Travel Time ◽  
Simulation Models ◽  
Performance Measure ◽  
Heterogeneous Traffic ◽  
Model Parameters ◽  
Mixed Traffic ◽  
Traffic Operations ◽  
Complex Interactions ◽  
Micro Simulation ◽  
Kolmogorov Smirnov

Calibration is an essential prerequisite to scenario evaluations using traffic micro-simulation models (TMMs). In the context of mixed-traffic operations, where different fast and slow moving vehicular modes form a heterogeneous environment, a well-calibrated model needs to give adequate importance to each mode to realistically replicate the complex interactions in the traffic stream. This paper presents a methodology for calibrating TMMs for such mixed-traffic conditions. A combination of vehicle mode-specific travel time distributions is adopted as the performance measure for the calibration. To aid practitioners, each step of the methodology is demonstrated using a VISSIM simulator considering a signalized corridor in the Kolkata metro city, India. The work includes genetic algorithm (GA)-based optimization for obtaining mode-specific parameter sets. The Kolmogorov-Smirnov test is carried out to compare the travel time distributions of different modes. The calibrated model is also validated considering several signalized approaches along the calibrated study corridor. The results show that the methodology is successful in developing a model for non-lane based mixed-traffic operations with vehicle mode-specific optimized parameter sets.

scholarly journals A Method for Joint Estimation of Homogeneous Model Parameters and Heterogeneous Desired Speeds

2020 ◽  
Vol 5 ◽  
Author(s):  
Fredrik Johansson
Keyword(s):  
Optimization Problem ◽  
Homogeneous Model ◽  
Heuristic Method ◽  
Large Data ◽  
Simulation Models ◽  
Joint Estimation ◽  
Model Parameters ◽  
Trajectory Data ◽  
Speed Distribution ◽  
Pedestrian Simulation

One of the main strengths of microscopic pedestrian simulation models is the ability to explicitly represent the heterogeneity of the pedestrian population. Most pedestrian populations are heterogeneous with respect to the desired speed, and the outputs of microscopic models are naturally sensitive to the desired speed; it has a direct effect on the flow and travel time, thus strongly affecting results that are of interest when applying pedestrian simulation models in practice. An inaccurate desired speed distribution will in most cases lead to inaccurate simulation results. In this paper we propose a method to estimate the desired speed distribution by treating the desired speeds as model parameters to be adjusted in the calibration together with other model parameters. This leads to an optimization problem that is computationally costly to solve for large data sets. We propose a heuristic method to solve this optimization problem by decomposing the original problem in simpler parts that are solved separately. We demonstrate the method on trajectory data from Stockholm central station and analyze the results to conclude that the method is able to produce a plausible desired speed distribution under slightly congested conditions.

Car-Following Model Calibration Based on Driving Simulator Data to Study Driver Characteristics and to Investigate Model Validity in Extreme Traffic Situations

2021 ◽  
pp. 036119812110326
Author(s):  
Moritz Berghaus ◽  
Eszter Kallo ◽  
Markus Oeser
Keyword(s):  
Driving Simulator ◽  
Driver Behavior ◽  
Field Studies ◽  
Driving Behavior ◽  
Model Parameters ◽  
Prediction Errors ◽  
Trajectory Data ◽  
Car Following ◽  
Model Validity ◽  
Simulator Study

In this paper we use traffic data from a driving simulator study to calibrate four different car-following models. We also present two applications for which the calibration results can be used. The first application relied on the advantage that driving simulator data also contain information on driver characteristics, for example, age, gender, or the self-assessment of driver behavior. By calibrating the models for each driver individually, the resulting model parameters could be used to analyze the influence of driver characteristics on driver behavior. The analysis revealed that certain characteristics, for example, self-identification as an aggressive driver, were reflected in the model parameters. The second application was based on the capability to simulate dangerous situations that require extreme driving behavior, which is often not included in datasets from real traffic and cannot be provoked in field studies. The model validity in these situations was analyzed by comparing the prediction errors of normal and extreme driving behavior. The results showed that all four car-following models underestimated the deceleration in an emergency braking scenario in which the drivers were momentarily shocked. The driving simulator study was validated by comparing the calibration results with those obtained from real trajectory data. We concluded that driving simulator data were suitable for the two proposed applications, although the validity of driving simulator studies must always be regarded.

scholarly journals Risk Modeling and Quantification of a Platoon in Mixed Traffic Based on the Mass-Spring-Damper Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luo Jiang ◽  
Jie Ji ◽  
Yue Ren ◽  
Hong Wang ◽  
Yanjun Huang
Keyword(s):  
Total Energy ◽  
Traffic Flow ◽  
Dominant Factor ◽  
Mixed Traffic ◽  
Quantitative Index ◽  
Car Following ◽  
Mixed Traffic Flow ◽  
Emergency Braking ◽  
Car Following Model ◽  
Mass Spring

Connected and automated vehicle (CAV) technologies have great potential to improve road safety. However, an emerging type of mixed traffic flow with human-driven vehicles (HDVs) and CAVs has also arisen in recent years. To improve the overall safety of this mixed traffic flow, a novel car-following model is proposed to control the driving behaviors of the above two types of vehicles in a platoon from the perspective of a mechanical system, mass-spring-damper (MSD) system. Furthermore, a quantitative index is proposed by incorporating the psychological field theory into the MSD model. The errors of spacing and speed in the car-following processes can be expressed as the accumulation of the virtual total energy, and the magnitude of the energy is used to reflect the danger level of vehicles in the mixed platoon. At the same time, the optimization model of minimum total energy is solved under the constraints of vehicle dynamics and the mechanical characteristics of the MSD system, and the optimal solutions are used as the parameters of the MSD car-following model. Finally, a mixed platoon composed of 3 CAVs and 2 HDVs without performing lane changing is tested using the driver-in-the-loop test platform. The test results show that, in the mixed platoon, CAVs can optimally adjust the intervehicle spacing by making full use of the braking distance, which also provides sufficient reaction time for the driver of HDV to avoid rear-end collisions. Furthermore, in the early stage of the emergency braking, the spacing error is the dominant factor influencing the car-following behaviors, but in the later stage of emergency braking, the speed error becomes the decisive factor of the car-following behaviors. These results indicate that the proposed car-following model and quantitative index are of great significance for improving the overall safety of the mixed traffic flow with CAVs and HDVs.

Calibration of Vehicle-Following Model Parameters Using Mixed Traffic Trajectory Data

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
P. Anusree Anand ◽  
Priyanka Atmakuri ◽  
Viswa Sri Rupa Anne ◽  
Gowri Asaithambi ◽  
Karthik K. Srinivasan ◽  
...  
Keyword(s):  
Model Parameters ◽  
Mixed Traffic ◽  
Trajectory Data

scholarly journals Comparison of Microscopic Drivers' Probabilistic Lane-changing Models With Real Traffic Microscopic Data

2012 ◽  
Vol 23 (4) ◽  
pp. 241-251
Author(s):  
Seyyed Mohammad Sadat Hoseini
Keyword(s):  
Probabilistic Models ◽  
Simulation Models ◽  
Real Data ◽  
Microscopic Level ◽  
Trajectory Data ◽  
Lane Changing ◽  
Traffic Models ◽  
Decision Factors ◽  
Vehicle Trajectory ◽  
Real Traffic

The difficulties of microscopic-level simulation models to accurately reproduce real traffic phenomena stem not only from the complexity of calibration and validation operations, but also from the structural inadequacies of the sub-models themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty in gathering accurate field data. This paper studies the traffic behaviour of individual drivers utilizing vehicle trajectory data extracted from digital images collected from freeways in Iran. These data are used to evaluate the four proposed microscopic traffic models. One of the models is based on the traffic regulations in Iran and the three others are probabilistic models that use a decision factor for calculating the probability of choosing a position on the freeway by a driver. The decision factors for three probabilistic models are increasing speed, decreasing risk of collision, and increasing speed combined with decreasing risk of collision. The models are simulated by a cellular automata simulator and compared with the real data. It is shown that the model based on driving regulations is not valid, but that other models appear useful for predicting the driver’s behaviour on freeway segments in Iran during noncongested conditions.

scholarly journals An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Shenzhen Ding ◽  
Xumei Chen ◽  
Zexin Fu ◽  
Fei Peng
Keyword(s):  
Traffic Simulation ◽  
Autonomous Vehicle ◽  
Significant Heterogeneity ◽  
Mixed Traffic ◽  
Trajectory Data ◽  
Data Set ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
Proposed Model ◽  
Car Following Model

The development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc., the current study of car-following behavior in mixed traffic has some limitations. Furthermore, actual data do not support its applicability to the Chinese traffic environment. To address this gap, this paper designs and organizes a car-following experiment in mixed traffic in Beijing, extracts the trajectory data of CAVs and HDVs based on video recognition, and reconstructs the extracted trajectory data using the Lagrangian theory and Kalman filter theory to ensure the accuracy of the data. Based on this data set, this paper develops an extended car-following model. The model considers the cooperation between drivers by reformulating the prospect theory (PT). The root mean square percentage error (RMSPE) is selected to calibrate and validate the parameters of the proposed model, and the results show that there is significant heterogeneity between CAVs and HDVs in mixed traffic, and the proposed model captures this heterogeneity well. The model presented in this paper provides theoretical support for microscopic traffic simulation in mixed traffic.

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