Citation

Wang D, Fan J, Xiao Z, Jiang H, Chen H, Zeng F, Li K. IEEE Trans. Intel. Transp. Syst. 2019; 20(10): 3623-3633.

Copyright

(Copyright © 2019, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2018.2878253

PMID

unavailable

Abstract

Private cars, a class of small motor vehicles usually registered by an individual for personal use, constitute the vast majority of city automobiles and hence significantly affect urban traffic. In particular, private cars tend to stop-and-wait (SAW) in specific regions during daily driving. This SAW behavior produces a spatiotemporal aggregation effect, which facilitates the formation of urban hot zones. In this paper, we investigate the SAW behavior and aggregation effect based on large-scale private car trajectory data. Specifically, motivated by the first law of geography, we leverage the kernel density estimation (KDE) method and extend it to three dimensions to capture the density distribution of the SAW data. Furthermore, according to the inherent relationship between the present SAW density and future SAW aggregation, we propose a 3D-KDE-based prediction model to characterize the dynamic spatiotemporal aggregation effect. In addition, we design a modified inertia weight particle swarm optimization (MIW-PSO) algorithm to determine the optimal weight coefficients and to avoid local optima during SAW prediction. Extensive experiments based on real-world private car SAW data validate the effectiveness of our method for discovering dynamic aggregation effects, therein outperforming the current methods in terms of the Kullback-Leibler (KL) divergence, mean absolute error (MAE), and root mean square error (RMSE). To the best of the authors' knowledge, our work is the first to utilize private car trajectory data to study the aggregation effect in urban environments, thereby being able to provide new insight into the study of traffic management and the evolution of urban traffic.

Language: en

Keywords

3D-KDE-based prediction model; Aggregation effect; Automobiles; dynamic aggregation effects; dynamic spatiotemporal aggregation effect; kernel density estimation; kernel density estimation method; large-scale private car trajectory data; mean square error methods; modified inertia weight particle swarm optimization algorithm; particle swarm optimisation; private car; private cars; Public transportation; real-world private car SAW data; road traffic control; Roads; Spatiotemporal phenomena; stop-and-wait; stop-and-wait (SAW); Surface acoustic waves; Trajectory; trajectory data; Urban areas; urban hot zones; urban traffic