An integrated framework for fully sampled vehicle trajectory reconstruction using a fused dataset

Jingfeng Ma, Claudio Roncoli, Gang Ren*, Yuanxiang Yang, Shunchao Wang, Jingcai Yu, Bingtong Wang

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

Abstrakti

Vehicle trajectories offer valuable insights for understanding traffic dynamics and optimising traffic control. However, the collection of fully-sampled vehicle trajectories is challenging due to unaffordable costs. To maximise the utility of sparse and limited trajectories, this study tailors an integrated framework for fully-sampled vehicle trajectory reconstruction. The proposed framework elaborates on a three-step work. Firstly, a piecewise cubic Hermite interpolating polynomial (PCHIP) is employed to reconstruct individual probe vehicle (PV) trajectories, and a piecewise order-changing model is proposed to capture overtaking dynamics. Secondly, a speed contour map is constructed to provide speed baselines for estimating undetected non-probe vehicle (NPV) trajectories on a region-by-region basis. Two candidate trajectories are estimated by conducting car-following (CF) model and inverse car-following (ICF) model, respectively. Thirdly, a weighted fusion model is designed to estimate NPV trajectories by integrating the model predictive control (MPC) algorithm. Comparative analysis proves that the combined model performs better than the pure CF model.

AlkuperäiskieliEnglanti
Sivumäärä30
JulkaisuTransportmetrica A: Transport Science
DOI - pysyväislinkit
TilaSähköinen julkaisu (e-pub) ennen painettua julkistusta - 9 tammik. 2025
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Sormenjälki

Sukella tutkimusaiheisiin 'An integrated framework for fully sampled vehicle trajectory reconstruction using a fused dataset'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä