TY - GEN
T1 - Multi-objective Optimization of Layout of Detectors and Floating Car Datum Requirement for Higher Efficiency of Traffic State Prediction
AU - Zhou, Xingyu
AU - Wang, Fei
AU - Yao, Fuxing
AU - Yang, Zihong
AU - Sun, Chao
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - To optimize the prediction error of speed field and the efficiency of traffic state prediction, a multi-objective optimization method considering the different physical and statistical properties of static detector data (SDD) and floating car data (FCD) is proposed to optimize the layout (the number and the corresponding location) of static detectors and the percentage of connected automated vehicles (CAVs) simultaneously. The optimization result is a set of Pareto optimal solutions providing the best trade-off between the layout of detectors and the percentage of the CAVs with reasonable prediction accuracy for different situations. In the detailed analysis, the comparative results of the predicted speed field before and after optimization indicates that: (1) the proposed optimization method improves the prediction accuracy by optimizing the layout of the detectors and the percentage of CAVs, (2) contradicting to intuitive knowledge, the increasing of the percentage of the CAVs may lead to the deterioration of the prediction accuracy as the FCD is lack of statistical representativeness.
AB - To optimize the prediction error of speed field and the efficiency of traffic state prediction, a multi-objective optimization method considering the different physical and statistical properties of static detector data (SDD) and floating car data (FCD) is proposed to optimize the layout (the number and the corresponding location) of static detectors and the percentage of connected automated vehicles (CAVs) simultaneously. The optimization result is a set of Pareto optimal solutions providing the best trade-off between the layout of detectors and the percentage of the CAVs with reasonable prediction accuracy for different situations. In the detailed analysis, the comparative results of the predicted speed field before and after optimization indicates that: (1) the proposed optimization method improves the prediction accuracy by optimizing the layout of the detectors and the percentage of CAVs, (2) contradicting to intuitive knowledge, the increasing of the percentage of the CAVs may lead to the deterioration of the prediction accuracy as the FCD is lack of statistical representativeness.
KW - layout optimization
KW - macroscopic traffic model
KW - multi-objective optimization
KW - traffic state prediction
UR - http://www.scopus.com/inward/record.url?scp=85125200239&partnerID=8YFLogxK
U2 - 10.1109/CCDC52312.2021.9602360
DO - 10.1109/CCDC52312.2021.9602360
M3 - Conference contribution
AN - SCOPUS:85125200239
T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
SP - 1910
EP - 1916
BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd Chinese Control and Decision Conference, CCDC 2021
Y2 - 22 May 2021 through 24 May 2021
ER -