Integrated macro-micro modelling for individual vehicle trajectory reconstruction using fixed and mobile sensor data

Chen, Xuejian; Yin, Juyuan; Qin, Guoyang; Tang, Keshuang; Wang, Yunpeng; Sun, Jian

doi:10.1016/j.trc.2022.103929

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…However, the error of trajectory will affect the accuracy of driving behaviour evaluation [15] . The erroneous position data will affect the estimation of kinematic quantities, and the measurement errors will also have adverse effects on the calibration of the car-following models [8] [17] . As Thiemann et al [8] have already pointed out, the distribution of acceleration investigated in NGSIM datasets is unrealistic.…”

Section: Introductionmentioning

confidence: 99%

“…The erroneous position data will affect the estimation of kinematic quantities, and the measurement errors will also have adverse effects on the calibration of the car-following models [8] [17] . As Thiemann et al [8] have already pointed out, the distribution of acceleration investigated in NGSIM datasets is unrealistic. Coifman and Li [17] critically evaluated vehicle trajectory video data from NGSIM video.…”

Section: Introductionmentioning

confidence: 99%

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Two step vehicle trajectory reconstruction strategy based on an improved outlier detection and data smoothing algorithm

Cao

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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Vehicle trajectory data contains abundant traffic information and plays a key role in traffic flow research. Due to unsuitable sensor position or perspective, there are some local errors in existing vehicle trajectory data sets. To solve this problem, a two-step vehicle trajectory data reconstruction strategy is proposed in this paper. In the first step, an outlier detection algorithm combines the cluster-based local outlier factor (CBLOF) and the k-means clustering algorithm (K-CBLOF) is proposed to identify outlier in object dataset. In the second step, Savitzky-Golay (SG) filter is selected to smooth the data flow containing noise. In this paper, the prototype data of I-80 highway in the open source trajectory data set NGSIM is used for case study. Taking vehicle 1 as an example, the speed and acceleration data of vehicle 1 are reconstructed. Time Windows with length of 11 and 21 are selected respectively to implement SG filter. The results show that when the time window value is 21, the trajectory data shows a better smoothed result, and the data noise can be obviously eliminated, which verifies that the trajectory data smoothing strategy proposed in this paper has a certain reliability.

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