Resurrecting the Lost Vehicle Trajectories of Treiterer and Myers with New Insights into a Controversial Hysteresis

2018 ◽  
Vol 2672 (20) ◽  
pp. 25-38 ◽  
Author(s):  
Benjamin Coifman ◽  
Lizhe Li ◽  
Wen Xiao
Keyword(s):  
High Speed ◽  
Original Data ◽  
Research Community ◽  
Trajectory Data ◽  
Data Set ◽  
Car Following ◽  
Traffic Flow Theory ◽  
Vehicle Trajectories ◽  
Time Space ◽  
Vehicle Trajectory

The 1974 paper by Treiterer and Myers is a seminal work in traffic flow theory. This longevity is in part because of the impressive collection of manually extracted vehicle trajectories. To date, only a few studies have rivaled the scale of the empirical vehicle trajectory data used in Treiterer and Myers. Their data collection used high-speed aerial photography and manual data reduction to follow hundreds of vehicles. In spite of the Herculean collection effort, the trajectory data set was never released and has since been lost. Fortunately, the plots survive and the present work re-extracts the vehicle trajectory data from the time–space diagrams. The discussion places the value of the data in context and then uses the data to put an end to decades of misinterpretation that started with Treiterer himself. The central thesis of Treiterer and Myers generated considerable interest: a hysteresis whereby drivers exhibit different fundamental behavior depending on whether they are entering or exiting a disturbance. There has been extensive debate about the authors’ findings in the literature, but without the original data set any interpretation has required considerable speculation. With the resurrected trajectories, this work reexamines the vehicles underlying the hysteresis and finally quells the speculation. Rather than arising from car following behavior, it turns out that the enigmatic progression arose from a combination of lane change maneuvers and unremarkable transitions into or out of the congested regime. On publication, the re-extracted data from this paper will be released to the research community.

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.

scholarly journals Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Juyuan Yin ◽  
Peng Chen ◽  
Keshuang Tang ◽  
Jian Sun
Keyword(s):  
Mobile Internet ◽  
Urban Traffic ◽  
Internet Technology ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Trajectory Data ◽  
Adaptive Signal Control ◽  
Vehicle Trajectories ◽  
Vehicle Trajectory ◽  
Adaptive Signal

With recent development of mobile Internet technology and connected vehicle technology, vehicle trajectory data are readily available and exhibit great potential to be used as an alternative data source for urban traffic signal control. In this study, a Queue Intensity Adaptive (QIA) algorithm is proposed, using vehicle trajectory data as the only input to perform adaptive signal control. First, a Kalman filter-based method is employed to estimate real-time queue state with vehicle trajectories. Then, based on queue intensity that quantifies queuing pressure, five control situations are defined, and different min-max optimization models are designed correspondingly. Last, a situation-aware signal control optimization procedure is developed to adapt intersection’s queue intensity. QIA algorithm optimizes phase sequence and green time simultaneously. One case study was conducted at a field intersection in Shenzhen, China. The results show that provided with 7.4% penetrated vehicle trajectories, QIA algorithm effectively prevented queue spillback by constraining temporal percentage of queue spillback under 2.4%. The performance of QIA was also compared with the algorithm in Synchro and Max Pressure (MP) method. It was found that compared with Synchro, the extreme queue intensity, temporal percentage of queue spillback, delay, and stops were decreased by 54.7%, 97%, 22.3%, and 45.1%, respectively, and compared with MP the above four indices were decreased by 16%, 61.5%, −1.8%, and 49.4%, respectively.

High-Speed VSD Imaging of Visually Evoked Cortical Waves: Decomposition Into Intra- and Intercortical Wave Motions

2002 ◽  
Vol 87 (3) ◽  
pp. 1499-1514 ◽  
Author(s):  
David M. Senseman ◽  
Kay A. Robbins
Keyword(s):  
High Speed ◽  
Visual Inspection ◽  
Wave Motion ◽  
Original Data ◽  
Visual Area ◽  
Relative Advantage ◽  
Data Set ◽  
Complex Wave ◽  
Image Projections ◽  
Cortical Waves

In the pond turtle, Pseudemys scripta elegans, visually evoked cortical waves propagate at different velocities within the primary visual area compared with waves that pass into the secondary visual area. In an effort to separate intra- and intercortical wave motions, movies of visually evoked cortical waves recorded by high-speed voltage-sensitive dye (VSD) imaging were subjected to Karhunen-Loéve (KL) decomposition. This procedure decomposes the VSD movies into a series of basis images that capture different spatial patterns of coherent activity. Most of the energy of the compound wave motion (>95%) was captured by the three largest basis images, M1,1 , M1,2 , and M2,1 . Based on visual comparison with maps of wave front latency, KL basis image M1,2 appears to capture the spread of depolarization within the primary visual area, whereas KL basis image M2,1 appears to capture the spread of depolarization from the primary into the secondary visual area. The contribution of different basis images to the intra- and intercortical wave motions was tested by reconstructing the response using different combinations of KL basis images. Only KL basis images M1,1 and M1,2 were needed to reconstruct intracortical wave motion, while basis images M1,1 and M2,1 were needed to reconstruct intercortical wave motion. It was also found that the direction and speed of wave propagation could be deduced by visual inspection of the basis image projections on to the original data set. The relative advantage of KL decomposition for the analysis of complex wave motions captured by VSD imaging is discussed.

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 Research on car-following model based on molecular dynamics

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199300
Author(s):  
Yanfeng Jia ◽  
Dayi Qu ◽  
Lewei Han ◽  
Lu Lin ◽  
Jiale Hong
Keyword(s):  
Traffic Flow ◽  
Potential Function ◽  
Molecular Model ◽  
Hot Spot ◽  
Data Set ◽  
Optimal Velocity ◽  
Car Following ◽  
Time Space ◽  
Car Following Model ◽  
Acceleration And Deceleration

The car-following model has always been a research hot spot in the field of traffic flow theory. Modeling the car-following behavior can quantify the longitudinal interaction between cars, thereby understanding the characteristics of traffic flow, and revealing the inherent mechanisms of traffic congestion and other traffic phenomena. In fact, there is an asymmetry problem in the driver’s acceleration and deceleration operation. The existing car-following model ignores the difference between the acceleration and deceleration of cars. To solve this problem, the cars driving on the road are compared to molecules with interactions. Based on the molecular interaction potential function and the wall potential function, we construct a molecular car-following model. We use NGSIM data set to calibrate the parameters of the model through the genetic algorithm. Finally, we analyze the evolution rule of the disturbance in the traffic flow in different states with the help of the time-space diagram, and compare the molecular model and the classical optimal velocity model. The results show that the molecular car-following model can better describe the car-following behavior from the micro level.

scholarly journals Real-Time Vehicle Trajectory Prediction for Traffic Conflict Detection at Unsignalized Intersections

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qianxia Cao ◽  
Zhongxing Zhao ◽  
Qiaoqiong Zeng ◽  
Zhengwu Wang ◽  
Kejun Long
Keyword(s):  
Real Time ◽  
Deep Neural Networks ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Target Vehicle ◽  
Time Prediction ◽  
Unsignalized Intersections ◽  
Vehicle Trajectories ◽  
Traffic Conflict ◽  
Vehicle Trajectory

Real-time prediction of vehicle trajectory at unsignalized intersections is important for real-time traffic conflict detection and early warning to improve traffic safety at unsignalized intersections. In this study, we propose a robust real-time prediction method for turning movements and vehicle trajectories using deep neural networks. Firstly, a vision-based vehicle trajectory extraction system is developed to collect vehicle trajectories and their left-turn, go straight, and right-turn labels to train turning recognition models and multilayer LSTM deep neural networks for the prediction task. Then, when performing vehicle trajectory prediction, we propose the vehicle heading angle change trend method to recognize the future move of the target vehicle to turn left, go straight, and turn right based on the trajectory data characteristics of the target vehicle before passing the stop line. Finally, we use the trained multilayer LSTM models of turning left, going straight, and turning right to predict the trajectory of the target vehicle through the intersection. Based on the TensorFlow-GPU platform, we use Yolov5-DeepSort to automatically extract vehicle trajectory data at unsignalized intersections. The experimental results show that the proposed method performs well and has a good performance in both speed and accuracy evaluation.

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.

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