Abstract
This research proposes a short-term highway traffic state prediction method based on a structural state space model, with the intention to provide a robust approach for obtaining accurate forecasts of traffic state under both recurring and non-recurring conditions. True traffic state is decomposed to three components, namely, regular traffic pattern, structural deviation, and random fluctuation. Particularly, the structural deviation term reflects the change of true traffic state from regular (historical) pattern, due to unexpected capacity reduction and/or demand variations. A polynomial trend is adopted to describe the temporal evolution of structural deviations across different time intervals. We derive an analytical form of structural deviations in a single bottleneck case based on cumulative flow count diagrams. The proposed model is incorporated in a Kalman filtering-based algorithmic framework, together with an adaptive scheme for determining the variances of random errors. A set of numerical experiments was conducted on two test beds in the northern Taiwan highway network. Experimental results show that the proposed approach is particularly superior to an ordinary Kalman filtering method presented in the literature under non-recurring conditions, highlighting the advantage of combining both the polynomial trend model and historical pattern into the proposed short-term traffic state prediction approach. © 2014
| Original language | English (US) |
|---|---|
| Pages (from-to) | 309-322 |
| Number of pages | 14 |
| Journal | Journal of Intelligent Transportation Systems: Technology, Planning, and Operations |
| Volume | 18 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jul 3 2014 |
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Keywords
- Kalman Filter
- Structural State Space Model
- Traffic State Estimation and Prediction
ASJC Scopus subject areas
- Information Systems
- Software
- Computer Science Applications
- Applied Mathematics
- Control and Systems Engineering
- Aerospace Engineering
- Automotive Engineering
Cite this
Short-term highway traffic state prediction using structural state space models. / Lu, Chung Cheng; Zhou, Xuesong.
In: Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol. 18, No. 3, 03.07.2014, p. 309-322.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Short-term highway traffic state prediction using structural state space models
AU - Lu, Chung Cheng
AU - Zhou, Xuesong
PY - 2014/7/3
Y1 - 2014/7/3
N2 - This research proposes a short-term highway traffic state prediction method based on a structural state space model, with the intention to provide a robust approach for obtaining accurate forecasts of traffic state under both recurring and non-recurring conditions. True traffic state is decomposed to three components, namely, regular traffic pattern, structural deviation, and random fluctuation. Particularly, the structural deviation term reflects the change of true traffic state from regular (historical) pattern, due to unexpected capacity reduction and/or demand variations. A polynomial trend is adopted to describe the temporal evolution of structural deviations across different time intervals. We derive an analytical form of structural deviations in a single bottleneck case based on cumulative flow count diagrams. The proposed model is incorporated in a Kalman filtering-based algorithmic framework, together with an adaptive scheme for determining the variances of random errors. A set of numerical experiments was conducted on two test beds in the northern Taiwan highway network. Experimental results show that the proposed approach is particularly superior to an ordinary Kalman filtering method presented in the literature under non-recurring conditions, highlighting the advantage of combining both the polynomial trend model and historical pattern into the proposed short-term traffic state prediction approach. © 2014
AB - This research proposes a short-term highway traffic state prediction method based on a structural state space model, with the intention to provide a robust approach for obtaining accurate forecasts of traffic state under both recurring and non-recurring conditions. True traffic state is decomposed to three components, namely, regular traffic pattern, structural deviation, and random fluctuation. Particularly, the structural deviation term reflects the change of true traffic state from regular (historical) pattern, due to unexpected capacity reduction and/or demand variations. A polynomial trend is adopted to describe the temporal evolution of structural deviations across different time intervals. We derive an analytical form of structural deviations in a single bottleneck case based on cumulative flow count diagrams. The proposed model is incorporated in a Kalman filtering-based algorithmic framework, together with an adaptive scheme for determining the variances of random errors. A set of numerical experiments was conducted on two test beds in the northern Taiwan highway network. Experimental results show that the proposed approach is particularly superior to an ordinary Kalman filtering method presented in the literature under non-recurring conditions, highlighting the advantage of combining both the polynomial trend model and historical pattern into the proposed short-term traffic state prediction approach. © 2014
KW - Kalman Filter
KW - Structural State Space Model
KW - Traffic State Estimation and Prediction
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UR - http://www.scopus.com/inward/citedby.url?scp=84903385295&partnerID=8YFLogxK
U2 - 10.1080/15472450.2013.836929
DO - 10.1080/15472450.2013.836929
M3 - Article
VL - 18
SP - 309
EP - 322
JO - Journal of Intelligent Transportation Systems
JF - Journal of Intelligent Transportation Systems
SN - 1547-2450
IS - 3
ER -