Route flow estimation based on the fusion of probe vehicle trajectory and automated vehicle identification data

Ma, Wanjing; Yuan, Jian; An, Kai Nan; Yu, Chunhui

doi:10.1016/j.trc.2022.103907

Cited by 11 publications

(3 citation statements)

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“…Both data are used as input to a quadratic programming framework [23]. Ma et al (2022) developed a route choice estimation framework considering both the probe vehicle trajectory and automated vehicle identification data as input, where route penetration rates are considered constraints. This framework is solved under the entropy maximization principle [24].…”

Section: A Link Flow Estimation With Data Fusionmentioning

confidence: 99%

“…Ma et al (2022) developed a route choice estimation framework considering both the probe vehicle trajectory and automated vehicle identification data as input, where route penetration rates are considered constraints. This framework is solved under the entropy maximization principle [24]. Brunauer et al proposed to solve a local network propagation problem between observed links based on propagation rules indicated by the probe vehicle trajectories [2].…”

Section: A Link Flow Estimation With Data Fusionmentioning

confidence: 99%

“…Since the entire travel paths of vehicles are captured, trajectory data have better spatial coverage than traffic volume data from loop detectors. It is thus desirable to combine these two data sources to improve link flow estimation, which has received great interest in recent years [16][17][18][23][24][25]. However, considering the limited market penetration rate of vehicle detection technologies and the data collection errors, the observed vehicle trajectory data may not reflect the true population trajectory distribution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating Link Flows in Road Networks With Synthetic Trajectory Data Generation: Inverse Reinforcement Learning Approach

Zhong

Kim

Zheng

2023

IEEE Open J. Intell. Transp. Syst.

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While traffic volume data from loop detectors have been the common data source for link flow estimation, the detectors only cover a subset of links. These days, other data sources such as vehicle trajectory data collected from vehicle tracking sensors are also incorporated. However, trajectory data are often sparse in that the observed trajectories only represent a small subset of the whole population, where the exact route sampling rate is unknown and may vary over space and time. In this paper, we develop a method that leverage these two limited data sources to enhance link flow estimation. This study proposes a novel generative modelling framework, where we formulate a vehicle's link-to-link movements as a sequential decision-making problem using the Markov Decision Process framework. We propose an Inverse Reinforcement Learning-based method, based on which synthetic population vehicle trajectories can be generated to estimate link flows across the whole network. The proposed method ensures the generated population vehicle trajectories are consistent with the observed traffic volume and trajectory data. The proposed generative modelling framework is compared to two existing methods in a synthetic road network and validated in a real road network.

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Section: A Link Flow Estimation With Data Fusionmentioning

confidence: 99%