TY - GEN
T1 - Towards Dynamic Bayesian Networks
T2 - 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
AU - Zhang, Haizheng
AU - Seshadri, Ravi
AU - Prakash, A. Arun
AU - Antoniou, Constantinos
AU - Pereira, Francisco Camara
AU - Ben-Akiva, Moshe
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/7
Y1 - 2018/12/7
N2 - A key component of Dynamic Traffic Assignment (DTA) systems is the online calibration of simulation parameters, which is crucial in generating accurate predictions of network states. A widely used approach for online calibration is the Kalman filter which allows for the incorporation of demand and supply parameters and any type of measurement data. This paper presents a Dynamic Bayesian Network extension for traditional Kalman filters with a technique called state augmentation. Although it has been discussed in the calibration literature, the usage and applicability were not fully investigated. The state augmentation technique is particularly useful for delayed systems, for example in large networks with high travel times. In this paper, we discuss state augmentation for Kalman filtering and illustrate its modeling advantages via a Dynamic Bayesian Network (DBN) representation. These advantages are demonstrated by a case study using the Singapore expressway network. The results indicate that employing state augmentation yields better estimation and prediction accuracy of traffic states, around 10% less error than the standard extended Kalman filter.
AB - A key component of Dynamic Traffic Assignment (DTA) systems is the online calibration of simulation parameters, which is crucial in generating accurate predictions of network states. A widely used approach for online calibration is the Kalman filter which allows for the incorporation of demand and supply parameters and any type of measurement data. This paper presents a Dynamic Bayesian Network extension for traditional Kalman filters with a technique called state augmentation. Although it has been discussed in the calibration literature, the usage and applicability were not fully investigated. The state augmentation technique is particularly useful for delayed systems, for example in large networks with high travel times. In this paper, we discuss state augmentation for Kalman filtering and illustrate its modeling advantages via a Dynamic Bayesian Network (DBN) representation. These advantages are demonstrated by a case study using the Singapore expressway network. The results indicate that employing state augmentation yields better estimation and prediction accuracy of traffic states, around 10% less error than the standard extended Kalman filter.
KW - OD estimation
KW - calibration
KW - constrained extended Kalman filter
KW - simulation and modeling
KW - time-delay system
UR - http://www.scopus.com/inward/record.url?scp=85060475335&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2018.8569926
DO - 10.1109/ITSC.2018.8569926
M3 - Conference contribution
AN - SCOPUS:85060475335
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1745
EP - 1750
BT - 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 November 2018 through 7 November 2018
ER -