Next Article in Journal
Effective Biopotential Signal Acquisition: Comparison of Different Shielded Drive Technologies
Next Article in Special Issue
Cloud Incubator Car: A Reliable Platform for Autonomous Driving
Previous Article in Journal
Mathematical Models of Electro-Magnetohydrodynamic Multiphase Flows Synthesis with Nano-Sized Hafnium Particles
Previous Article in Special Issue
Study on Driving Decision-Making Mechanism of Autonomous Vehicle Based on an Optimized Support Vector Machine Regression
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(2), 277; https://doi.org/10.3390/app8020277

Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

1
College of Information Science & Technology, Donghua University, Shanghai 201620, China
2
Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China
*
Author to whom correspondence should be addressed.
Received: 26 October 2017 / Revised: 18 January 2018 / Accepted: 9 February 2018 / Published: 12 February 2018
Full-Text   |   PDF [1499 KB, uploaded 22 February 2018]   |  

Abstract

With the rapid increase of private vehicles, traffic congestion has become a worldwide problem. Various models have been proposed to undertake traffic prediction. Among them, autoregressive integrated moving average (ARIMA) models are quite popular for their good performance (simple, low complexity, etc.) in traffic prediction. Localized Space-Time ARIMA (LSTARIMA) improves ARIMA’s prediction accuracy by extending the widely used STARIMA with a dynamic weight matrix. In this paper, a localized space-time autoregressive (LSTAR) model was proposed and a new parameters estimation method was formulated based on the LSTARIMA model to reduce computational complexity for real-time prediction purposes. Moreover, two theorems are given and verified for parameter estimation of our proposed LSTAR model. The simulation results showed that LSTAR provided better prediction accuracy when compared to other time series models such as Shift, autoregressive (AR), seasonal moving average (Seasonal MA), and Space-Time AR (STAR). We found that the prediction accuracy of LSTAR was a bit lower than the LSTARIMA model in the simulation results. However, the computational complexity of the LSTAR model was also lower than the LSTARIMA model. Therefore, there exists a tradeoff between the prediction accuracy and the computational complexity for the two models. View Full-Text
Keywords: LSTAR; STARIMA; parameters estimation; traffic flow prediction; urban road network LSTAR; STARIMA; parameters estimation; traffic flow prediction; urban road network
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Chen, J.; Li, D.; Zhang, G.; Zhang, X. Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks. Appl. Sci. 2018, 8, 277.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top