TY - GEN
T1 - Using link travel time co variance information to predict dynamic journey times in stochastic road networks
AU - Ho, H. W.
AU - Lam, William H.K.
AU - Tam, Mei Lam
N1 - Publisher Copyright:
© 2017 Hong Kong Society for Transportation Studies Limited. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Journey time prediction is a crucial component in advanced traveler information systems for helping travelers in making their travel decisions. This paper investigates the journey time prediction problem in road network with stochastic journey times and link flows. The proposed prediction framework consists of two sub-modules. The first one is a reliability-based dynamic traffic assignment model to establish a database for the historical traffic conditions, while the other sub-module, which is a multi-level k-NN model for predicting journey times based on the historical records in the database. A Sioux Falls road network example is used to demonstrate the accuracy, efficiency and robustness of the proposed framework for the journey time prediction problem in stochastic network with uncertainties.
AB - Journey time prediction is a crucial component in advanced traveler information systems for helping travelers in making their travel decisions. This paper investigates the journey time prediction problem in road network with stochastic journey times and link flows. The proposed prediction framework consists of two sub-modules. The first one is a reliability-based dynamic traffic assignment model to establish a database for the historical traffic conditions, while the other sub-module, which is a multi-level k-NN model for predicting journey times based on the historical records in the database. A Sioux Falls road network example is used to demonstrate the accuracy, efficiency and robustness of the proposed framework for the journey time prediction problem in stochastic network with uncertainties.
KW - Dynamic traffic assignment
KW - Effective path journey time
KW - Journey time prediction
KW - K-nearest neighborhood
KW - Travel time covariance
UR - http://www.scopus.com/inward/record.url?scp=85050584034&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85050584034
T3 - Transport and Society - Proceeding of the 22nd International Conference of Hong Kong Society for Transportation Studies, HKSTS 2017
SP - 159
EP - 166
BT - Transport and Society - Proceeding of the 22nd International Conference of Hong Kong Society for Transportation Studies, HKSTS 2017
A2 - Chen, Anthony
A2 - Sze, Tony N.N.
T2 - 22nd International Conference of Hong Kong Society for Transportation Studies: Transport and Society, HKSTS 2017
Y2 - 9 December 2017 through 11 December 2017
ER -