scholarly journals Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter

2019 ◽  
Vol 20 (2) ◽  
pp. 114-122 ◽  
Author(s):  
Asmâa Ouessai ◽  
Mokhtar Keche
Keyword(s):  
Kalman Filter ◽  
Real World ◽  
Road Traffic ◽  
Divided Difference ◽  
Estimation Error ◽  
Second Order ◽  
Parameters Estimation ◽  
Highway Traffic ◽  
Traffic State ◽  
State Identification

Abstract Reliable road traffic state identification systems should be designed to provide accurate traffic state information anywhere and anytime. In this paper we propose a road traffic classification system, based on traffic variables estimated using the second order Divided Difference Kalman Filter (DDKF2). This filter is compared with the Extended Kalman Filter (EKF) using both simulated and real-world dataset of highway traffic. Monte-Carlo simulations indicate that the DDKF2 outperforms the EKF filter in terms of parameters estimation error. The real-word evaluation of the DDKF2 filter in terms of classification rate confirms that this filter is promising for real-world traffic state identification systems.

Real-time road traffic state prediction based on ARIMA and Kalman filter

2017 ◽  
Vol 18 (2) ◽  
pp. 287-302 ◽  
Author(s):  
Dong-wei Xu ◽  
Yong-dong Wang ◽  
Li-min Jia ◽  
Yong Qin ◽  
Hong-hui Dong
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Road Traffic ◽  
Traffic State ◽  
State Prediction

Signal Timing Parameters Estimation for Intersections using Floating Car Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 189-201 ◽  
Author(s):  
Zelong Du ◽  
Xintao Yan ◽  
Jinqing Zhu ◽  
Weili Sun
Keyword(s):  
Real World ◽  
Estimation Error ◽  
Absolute Error ◽  
Time Of Day ◽  
Parameters Estimation ◽  
Data Availability ◽  
Estimation Methods ◽  
Traffic Light ◽  
Real World Data ◽  
Floating Car Data

Signal phase and timing (SPaT) information is critical for many in-vehicle applications. However, it is challenging and time-consuming to acquire city-wide SPaT information from local traffic management agencies directly. A significant limitation of existing SPaT information estimation methods in the literature is that they can only be applied to a specified time-of-day (TOD) period. In the real-world, however, different TOD timing plans are used to accommodate fluctuations in traffic demands. In this paper, we propose a novel method for traffic light parameter estimation based on floating car data, which features recognizing TOD breakpoints and can thus be applied to intersections with multi-TOD timing plans. Also, good estimation of TOD breakpoints leads to more data availability for estimation of other parameters. The proposed method is tested with real-world data collected from the DiDi on-line hailing platform in China. The filed test results show promising accuracy. The absolute error of green duration is within 3 s in daytime and the estimation error of TOD breakpoints is within 15 min.

scholarly journals Comparative Study of a Super-Twisting Sliding Mode Observer and an Extended Kalman Filter for a Freeway Traffic System

2015 ◽  
Vol 15 (2) ◽  
pp. 141-158 ◽  
Author(s):  
H. Majid ◽  
H. Abouaïssa
Keyword(s):  
Kalman Filter ◽  
Comparative Study ◽  
Extended Kalman Filter ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Sliding Mode Observer ◽  
State Variables ◽  
Highway Traffic ◽  
Traffic State ◽  
Geometric Conditions

Abstract Traffic state estimation represents one of the important ingredients for traffic prediction and forecasting. The work presented in this paper deals with the estimation of traffic state variables (density and speed), using the so called Super- Twisting Sliding Mode Observer (STSM). Several numerical simulations, using simulated and real data, show the relevance of the proposed approach. In addition, a comparative study with the Extended Kalman Filter (EKF) is carried-out. The comparison indices concern convergence and stability, dynamic performance and robustness. The design of the two observers is achieved using a nonlinear second order traffic flow model in the same highway traffic and geometric conditions.

scholarly journals Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Deng Ming-jun ◽  
Qu Shi-ru
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Road Traffic ◽  
Short Term ◽  
Traffic Forecasting ◽  
Traffic State ◽  
Forecasting Error ◽  
Trend Forecasting ◽  
State Variation ◽  
Fuzzy State

Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting but is deficient in the expression of time hysteretically. This paper proposed an approach that combining fuzzy state transform and KF forecasting model. In considering the advantage of the two models, a weight combination model is proposed. The minimum of the sum forecasting error squared is regarded as a goal in optimizing the combined weight dynamically. Real detection data are used to test the efficiency. Results indicate that the method has a good performance in terms of short-term traffic forecasting.

Sign in / Sign up

Export Citation Format

Share Document