Next Article in Journal
An Analytical Model for Describing the Power Coupling Ratio between Multimode Fibers with Transverse Displacement and Angular Misalignment in an Optical Fiber Bend Sensor
Previous Article in Journal
Estimation of the Precision of a Structured Light System in Oil Paintings on Canvas
Open AccessArticle

FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data

1
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2
Yuanpei College, Shaoxing University, Shaoxing 312000, China
3
Zhijiang College, Zhejiang University of Technology, Shaoxing 312000, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(22), 4967; https://doi.org/10.3390/s19224967
Received: 16 October 2019 / Revised: 10 November 2019 / Accepted: 12 November 2019 / Published: 14 November 2019
(This article belongs to the Section Sensor Networks)
The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate. View Full-Text
Keywords: intelligent transportation system; average speed prediction; GPS data; RLS-EKF algorithm intelligent transportation system; average speed prediction; GPS data; RLS-EKF algorithm
Show Figures

Figure 1

MDPI and ACS Style

Zhu, D.; Shen, G.; Liu, D.; Chen, J.; Zhang, Y. FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data. Sensors 2019, 19, 4967. https://doi.org/10.3390/s19224967

AMA Style

Zhu D, Shen G, Liu D, Chen J, Zhang Y. FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data. Sensors. 2019; 19(22):4967. https://doi.org/10.3390/s19224967

Chicago/Turabian Style

Zhu, Difeng; Shen, Guojiang; Liu, Duanyang; Chen, Jingjing; Zhang, Yijiang. 2019. "FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data" Sensors 19, no. 22: 4967. https://doi.org/10.3390/s19224967

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop