An analytical model for quantifying the efficiency of traffic-data collection using instrumented vehicles

Cao, Peng; Xiong, Zhiqiang; Liu, Xiaobo

doi:10.1016/j.trc.2022.103558

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…All the noise variances of ω , γ are set as Table 2 which determines the matrix Q and R . And the model parameters are set as Table 3, in which, τ , α , β refer to Wang and Papageorgiou (6) , δ refers to Papageorgiou et al ( 41 ), a refers to Cao et al ( 15 ), and v f and ρ jam refer to the US Department of Transportation Federal Highway Administration ( 29 ).…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…where ρ i , j c denotes the density of ICVs. Equation 18 describes the relationship between the penetration rate of ICVs and the percentage of the detected vehicles, where a is the parameter ( 15 ).…”

Section: Methodsmentioning

confidence: 99%

“…2. ICVD with various penetration rates of ICVs are obtained using a simulation ICVD acquisition method proposed in Cao et al (15) since such data in the real world are currently unavailable. The basic idea of the ICVD acquisition method is to simulate ICVs using given micro trajectory data sets (e.g., NGSIM data sets), which include the complete trajectories of all vehicles in the traffic flow.…”

Section: Data Descriptionsmentioning

confidence: 99%

“…Their methods still need to rely on continuous trajectories from vehicles in pairs with a car-following relation; however, most ICVD are discontinuous and unpaired because of occlusions and lane changes. Cao et al ( 15 ) proposed an analytical model for quantifying the efficiency of traffic-data collection using ICV. They established a theory to predict the percentage of trajectory points that can be collected by a certain percentage of ICVs within the entire traffic flow.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, emerging intelligent connected vehicles (ICVs) have demonstrated great potential for collecting microscopic traffic data in their collection efficiency and data accuracy (14,15). ICV refers to a new generation of vehicles equipped with advanced sensors, controllers, and actuators, combined with modern network communication technologies (16)(17)(18)(19).…”

mentioning

confidence: 99%

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Estimating Freeway Lane-Level Traffic State with Intelligent Connected Vehicles

Liu

Zhang

Miwa

et al. 2022

Transportation Research Record: Journal of the Transportation R

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This paper proposes a methodology for estimating lane-level traffic state for freeways by fusing data from intelligent connected vehicles (ICVs) with fixed detector data (FDD) and probe vehicle data (PVD). With microscopic vehicle trajectories of ICVs and their surrounding vehicles, the proposed methodology integrates a multilane traffic flow model into the data assimilation framework based on extended Kalman filter (EKF), in which traffic measurement models are formulated for ICV data, PVD, and FDD, respectively, to fit their different characteristics. Simulation experiments are conducted to test the performance of the proposed methodology with various penetration rates of ICVs, using a set of simulated ICV data based on the Next Generation SIMulation (NGSIM) data sets. The results demonstrate that by utilizing only 3% to 5% ICVs in the mixed traffic, the proposed methodology could produce an accurate estimate of lane-level traffic speed and a reasonable estimate of lane-level traffic density.

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