Heterogeneity in car-following behavior: Theory and empirics

Ossen, Saskia; Hoogendoorn, Serge P.

doi:10.1016/j.trc.2010.05.006

Cited by 184 publications

(97 citation statements)

References 19 publications

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“…Other studies investigated the driver heterogeneity during carfollowing process [9,10]. It is believed that accommodating the heterogeneity within the driver population is inevitable for producing a more precise car-following model (Kim et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Characterizing Heterogeneity in Drivers’ Merging Maneuvers Using Two-Step Cluster Analysis

Sun

2018

Journal of Advanced Transportation

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In order to investigate the heterogeneity in merging behaviors on freeways, a novel data mining tool, called two-step cluster analysis, is applied to the merging maneuvers (namely, initial speed, merging speed, and merging position). Merging maneuvers of 370 drivers collected from the NGSIM dataset are automatically and optimally segmented into four clusters (Early Merging Drivers at High Speed, Early Merging Drivers at Low Speed, Late Merging Drivers at Low Speed, and Late Merging Drivers at High Speed) by the two-step cluster analysis. Hypothesis test confirms the significant differences in merging maneuvers between different clusters. The clustered data are used to find the best corresponding fitting distributions. Seven distributions (Normal, Log-normal, Student's , Logistic, Log-Logistic, Gamma, and Weibull) are considered for each cluster and the Kolmogorov-Smirnov test statics are used to select the best fitted distributions. It is found that merging drivers may merge either early or late, under congestion or uncongested traffic condition. Further analysis of merging durations shows that Late Merging Drivers use significantly shorter time than Early Merging Drivers to finish the merging maneuver, no matter if they are at high or at low speed. Hypothesis test of accepted lead gaps and lag gaps indicate that merging drivers are more sensitive to the lag gaps under congestion. The proposed method can automatically identify the heterogeneity in merging drivers and the results obtained in this paper can be used to enhance the accuracy of the merge behavior models in microscopic simulation software.

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Section: Introductionmentioning

confidence: 99%

Characterizing Heterogeneity in Drivers’ Merging Maneuvers Using Two-Step Cluster Analysis

Sun

2018

Journal of Advanced Transportation

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“…And given that the values of the anticipation parameters and depend on many factors such as the age, the size of vehicles, physical fitness level of drivers, experience, and the environment of road, the exact distribution of the anticipation parameters and relevant analysis are worth more discussion in future research. For dynamics of a traffic flow, whether disturbances will eventually expand or disappear and whether the traffic flow is stable or not are very important [36]. To check the stability behavior of the proposed model and to further analyze the impact of anticipation characteristic and leading vehicle's acceleration information on the stability of traffic flow, the numerical simulations are carried out under a periodic boundary condition by using the same parameters as in [28].…”

Section: Starting and Braking Processesmentioning

confidence: 99%

A New Car-Following Model considering Driving Characteristics and Preceding Vehicle’s Acceleration

Zhang

et al. 2017

Journal of Advanced Transportation

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In the past decades, many improved car-following models based on the full velocity difference (FVD) model have been developed. But these models do not consider the acceleration of leading vehicle. Some of them consider individual anticipation behavior of drivers, but they either do not quantitatively determine the types of driving or artificially divide the driving types rather than deriving them from actual traffic data. In this paper, driver's driving styles are firstly categorized based on actual traffic data via data mining and clustering algorithm. Secondly, a new car-following model based on FVD model is developed, taking into account individual anticipation effects and the acceleration of leading vehicle. The effect of driving characteristics and leading vehicle's acceleration on car-following behavior is further analyzed via numerical simulation. The results show that considering the acceleration of preceding vehicle in the model improves the stability of traffic flow and different driving characteristics have different influence on the stability of traffic flow.

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“…Ossen et al [13] studied multiple car following models and concluded that performance of more complex models differs between drivers. In a later study [14], Ossen et al studied trajectories of cars and trucks and concluded that reactions of…”

Section: Related Workmentioning

confidence: 99%

Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

Sazara

Nezafat

Cetin

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-LIDAR has become an important part of many autonomous vehicles with its advantages on distance measurement and obstacle detection. LIDAR produces point clouds which have important information about surrounding environment. In this paper, we collected trajectory data on a two lane urban road using a Velodyne VLP-16 Lidar. Due to dynamic nature of data collection and limited range of the sensor, some of these trajectories have missing points or gaps. In this paper, we propose a novel method for recovery of missing vehicle trajectory data points using microscopic traffic flow models. While short gaps (less than 5 seconds) can be recovered with simple linear regression, and longer gaps are recovered with the proposed method that makes use of car following models calibrated by assigning weights to known points based on proximity to the gaps. Newell's, Pipes, IDM and Gipps' car following models are calibrated and tested with the ground truth trajectory data from LIDAR and NGSIM I-80 dataset. Gipps' calibrated model yielded the best result.

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Heterogeneity in car-following behavior: Theory and empirics

Cited by 184 publications

References 19 publications

Characterizing Heterogeneity in Drivers’ Merging Maneuvers Using Two-Step Cluster Analysis

Characterizing Heterogeneity in Drivers’ Merging Maneuvers Using Two-Step Cluster Analysis

A New Car-Following Model considering Driving Characteristics and Preceding Vehicle’s Acceleration

Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

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