Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway

Yao, Zhihong; Li, Meng; Jiang, Yangsheng; Tang, Youhua

doi:10.1049/itr2.12294

Cited by 9 publications

(4 citation statements)

References 71 publications

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“…Yao et al. [2] propose a fully sampled trajectory reconstruction method for traffic mixed with RVs, CVs and CAVs. Considering the minimum safety distance constraints between vehicles, they develop an optimization model for minimizing the impact on the acceleration of the known vehicles in order to obtain the number of inserted RVs.…”

Section: Papers In the Special Issuementioning

confidence: 99%

Guest Editorial: Modelling, operation and management of traffic mixed with connected and automated vehicles

Zong,

Zhong,

et al. 2024

IET Intelligent Trans Sys

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All of the papers selected for this special issue show that the field of modelling, operation and management of traffic mixed with CAVs is steadily moving forward. Nevertheless, from the perspective of future technological development, there remains a source of inspiration for innovation research in the years to come, mainly reflected in three aspects: (1) the dynamic coupling relationship between the cyber and physical network of the transportation system in an intelligent network environment; (2) the driving characteristics of RVs in mixed traffic; and (3) traffic prediction and control at the trip chain level from a macro perspective.

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Section: Papers In the Special Issuementioning

confidence: 99%

Guest Editorial: Modelling, operation and management of traffic mixed with connected and automated vehicles

Zong,

Zhong,

et al. 2024

IET Intelligent Trans Sys

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“…where v n− 1 (t) ′ represents the estimated speed of the leading vehicle V n . Te coefcient λ was calibrated by Goodall et al [27] in 2013 to be 0.162 based on NGSIM data. Using a preset parameter model to estimate the speed of the leading vehicle in free fow and stable following situations may work well.…”

Section: Estimation Of the Lead Vehicle Speed As Shown Inmentioning

confidence: 99%

“…By comparing the actual acceleration of vehicles with the expected acceleration calculated based on the Wiedemann model, it was determined that there were undetected manually driven vehicles between two CAVs when the diference in acceleration exceeded a certain threshold [26]. Yao et al [27] employed the IDM to optimize the insertion position of human-driven vehicles, with the primary objective of minimizing the mean squared error between the actual acceleration of the leading vehicle and the estimated acceleration. Tis method, based on car-following models, not only addresses the limitations of data-driven approaches but also signifcantly enhances the accuracy of estimating the position of artifcially driven vehicles.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Speed and Position Estimation considering Microscopic Heterogeneous Car-Following Characteristics in Connected Vehicle Environments

Xia,

Liu,

et al. 2023

Journal of Advanced Transportation

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This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.

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“…Several authors have explored such topics and found that with sufficient data processing and traffic flow assumptions low penetrations of connected vehicles can be used to reconstruct the traffic state ( 36 ). Generally, such studies use car following models to make assumptions about the trajectories of intermediate vehicles when penetration rates are low ( 37 , 38 ). In fact, speeds from connected vehicle data could be incorporated into the methodology of this study even when penetrations are low (see the subsection covering speedmap creation for further details).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Evaluation Methodology for Active Traffic Management Systems Utilizing Sparse Speed Data

Robbennolt

Hourdos

2023

Transportation Research Record: Journal of the Transportation R

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Many active traffic management systems and transportation systems management and operations strategies have been evaluated for safety based on crash reduction over time. These long-term studies are effective in showing the safety benefits of new systems, but do not often quantify other factors such as travel time, the extent of congestion, or the environmental impacts. Building on previous research into spatiotemporal interpolation of speed data, this methodology developed a mathematical representation of the speed and acceleration potential of the traffic stream given the sparse speed data from point sensors. This high-resolution estimate of traffic state could be used to construct trajectories of vehicles the could include data on vehicle speed and acceleration at each location in space and point in time. This methodology is general, and the trajectories could be used to evaluate traffic flow in several different ways. In the case study provided, trajectories were used to evaluate the ability of a queue warning algorithm to detect and warn drivers about unsafe conditions. Other potential applications include utilizing these trajectories to calculate fuel consumption, travel times, and speed variability to determine how new systems affect fundamental traffic characteristics.

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Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway

Cited by 9 publications

References 71 publications

Guest Editorial: Modelling, operation and management of traffic mixed with connected and automated vehicles

Guest Editorial: Modelling, operation and management of traffic mixed with connected and automated vehicles

Vehicle Speed and Position Estimation considering Microscopic Heterogeneous Car-Following Characteristics in Connected Vehicle Environments

Data-Driven Evaluation Methodology for Active Traffic Management Systems Utilizing Sparse Speed Data

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