TY - GEN
T1 - A goal-prioritized algorithm for additional route deployment on existing mass transportation system
AU - Lin, Fandel
AU - Hsieh, Hsun Ping
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Multi-criteria path planning is an important combinatorial optimization problem with broad real-world applications. Finding the Pareto-optimal set of paths ideal for all requiring features is time-consuming and unclear to obtain the subset of optimal paths efficiently for multiple origin states in the planning space. Meanwhile, due to the rise of deep learning, hybrid systems of computational intelligence thrive in recent years. When facing non-monotonic data or heuristics derived from pre-trained neural networks, most of the existing methods for the one-to-all path problem fail to find an ideal solution. We employ Gaussian mixture model to propose a target-prioritized searching algorithm called Multi-Source Bidirectional Gaussian-Prioritized Spanning Tree (BiasSpan) in solving this non-monotonic multi-criteria route planning problem given constraints including range, must-visit vertices, and the number of recommended vertices. Experimental results on mass transportation system in Tainan and Chicago cities show that BiasSpan outperforms comparative methods from 7% to 24% and runs in a reasonable time compared to state-of-art route-planning algorithms.
AB - Multi-criteria path planning is an important combinatorial optimization problem with broad real-world applications. Finding the Pareto-optimal set of paths ideal for all requiring features is time-consuming and unclear to obtain the subset of optimal paths efficiently for multiple origin states in the planning space. Meanwhile, due to the rise of deep learning, hybrid systems of computational intelligence thrive in recent years. When facing non-monotonic data or heuristics derived from pre-trained neural networks, most of the existing methods for the one-to-all path problem fail to find an ideal solution. We employ Gaussian mixture model to propose a target-prioritized searching algorithm called Multi-Source Bidirectional Gaussian-Prioritized Spanning Tree (BiasSpan) in solving this non-monotonic multi-criteria route planning problem given constraints including range, must-visit vertices, and the number of recommended vertices. Experimental results on mass transportation system in Tainan and Chicago cities show that BiasSpan outperforms comparative methods from 7% to 24% and runs in a reasonable time compared to state-of-art route-planning algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85100893989&partnerID=8YFLogxK
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U2 - 10.1109/ICDM50108.2020.00137
DO - 10.1109/ICDM50108.2020.00137
M3 - Conference contribution
AN - SCOPUS:85100893989
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1130
EP - 1135
BT - Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
A2 - Plant, Claudia
A2 - Wang, Haixun
A2 - Cuzzocrea, Alfredo
A2 - Zaniolo, Carlo
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Data Mining, ICDM 2020
Y2 - 17 November 2020 through 20 November 2020
ER -