TY - JOUR
T1 - Improving Scalability of Generic Online Calibration for Real-Time Dynamic Traffic Assignment Systems
AU - Prakash, A. Arun
AU - Seshadri, Ravi
AU - Antoniou, Constantinos
AU - Pereira, Francisco C.
AU - Ben-Akiva, Moshe
N1 - Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2018.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - Flexible calibration of dynamic traffic assignment (DTA) systems in real time has important applications in effective traffic management. However, the existing approaches are either limited to small networks or to a specific class of parameters. In this light, this study presents a framework to systematically reduce the dimension of the generic online calibration problem, making it more scalable. Specifically, a state–space formulation of the problem in the reduced dimension space is proposed. Following this the problem is solved using the constrained extended Kalman filter, which is made tractable because of the low dimensionality of the formulated problem. The effectiveness of the proposed approach is demonstrated using a real-world network leading to better state estimation by 13% and better state predictions by 11%—with a 50 fold dimensionality reduction. Insights into choosing the right degree of dimensionality reduction are also discussed. This work has the potential for a more widespread application of real-time DTA systems in practice.
AB - Flexible calibration of dynamic traffic assignment (DTA) systems in real time has important applications in effective traffic management. However, the existing approaches are either limited to small networks or to a specific class of parameters. In this light, this study presents a framework to systematically reduce the dimension of the generic online calibration problem, making it more scalable. Specifically, a state–space formulation of the problem in the reduced dimension space is proposed. Following this the problem is solved using the constrained extended Kalman filter, which is made tractable because of the low dimensionality of the formulated problem. The effectiveness of the proposed approach is demonstrated using a real-world network leading to better state estimation by 13% and better state predictions by 11%—with a 50 fold dimensionality reduction. Insights into choosing the right degree of dimensionality reduction are also discussed. This work has the potential for a more widespread application of real-time DTA systems in practice.
UR - http://www.scopus.com/inward/record.url?scp=85060481070&partnerID=8YFLogxK
U2 - 10.1177/0361198118791360
DO - 10.1177/0361198118791360
M3 - Article
AN - SCOPUS:85060481070
SN - 0361-1981
VL - 2672
SP - 79
EP - 92
JO - Transportation Research Record
JF - Transportation Research Record
IS - 48
ER -