An Analytical Model for Quantifying the Efficiency of Traffic-Data Collection Using Instrumented Vehicles

Cao, Peng; Xiong, Zhiqiang; Liu, Xiaobo

doi:10.2139/ssrn.3883914

Cited by 2 publications

(6 citation statements)

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“…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: 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%

“…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 ( 16–19 ).…”

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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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Section: Experiments and Results Analysismentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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

Self Cite

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show abstract

“…Cooperative control of multiple lanes contains the traffic information collection by roadside equipment and the traffic-induced and synergy by vehicle-road collaborative and information interaction. Based on the similarity matrix flow of road section, the two-objective optimization model was established which could realize high precision traffic information collection [1] . Cooperative vehicle infrastructure system technology and gap acceptance theory were exploited to build the information exchange framework between vehicle and vehicle, vehicle and road.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Control between Off-Ramp of Expressway and Side Road

2022

jecet

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The traffic jams frequently occur in the expressway off-ramp during morning and evening peak hours. Seriou's merging conflict of vehicles from the expressway offramp and side road is the mainly reason. Traffic jams spread upstream which will further affect road traffic efficiency. According to the actual traffic operating situation of expressway and side road, the cooperative control method between off-ramp of expressway and side road is proposed. To support the cooperative control system, the method of organic combination of ground loop, road marking and signal control is proposed, which provides a new perspective to deal with traffic merging under the guidance of smart traffic. In the end, the efficiency of expressway traffic from simulation is compared with the current actual traffic efficiency, and the result shows a perfect improvement.

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An Analytical Model for Quantifying the Efficiency of Traffic-Data Collection Using Instrumented Vehicles

Cited by 2 publications

References 0 publications

Estimating Freeway Lane-Level Traffic State with Intelligent Connected Vehicles

Estimating Freeway Lane-Level Traffic State with Intelligent Connected Vehicles

Cooperative Control between Off-Ramp of Expressway and Side Road

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