Exploring public transport transfer opportunities for Pareto search of multicriteria journeys
Multimodal public transport networks (MMPTNs) in modern cities are becoming increasingly complex. This makes finding optimal journey routes challenging due to a large number of transfer options that need to be properly considered. Furthermore, the complexity of the problem is compounded when multipl...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/179466 |
| _version_ | 1826110739747700736 |
|---|---|
| author | He, Peilan Jiang, Guiyuan Lam, Siew-Kei Sun, Yidan Ning, Fangxin |
| author2 | College of Computing and Data Science |
| author_facet | College of Computing and Data Science He, Peilan Jiang, Guiyuan Lam, Siew-Kei Sun, Yidan Ning, Fangxin |
| author_sort | He, Peilan |
| collection | NTU |
| description | Multimodal public transport networks (MMPTNs) in modern cities are becoming increasingly complex. This makes finding optimal journey routes challenging due to a large number of transfer options that need to be properly considered. Furthermore, the complexity of the problem is compounded when multiple conflicting travel criteria are considered (e.g., travel time, walking distance, travel fare, etc.). This paper proposes a transfer graph (TG) model to explore the transfer opportunities of the MMPTN to support efficient journey route planning. TG considers all possible transfer opportunities, while employing a representative mechanism to optimize the TG structure that supports efficient route planning algorithms. Based on the proposed TG, we develop two exact algorithms to search the Pareto-optimal solutions for multi-criteria journey planning (MCJP) over the MMPTN. The first algorithm runs faster by eliminating many partial solutions at an early stage, which is more suited for lowering computation time at the expense of marginal degradation in output quality. In contrast, the second algorithm provides a more dependable solution by incorporating accurate journey time prediction that caters to the evolving traffic conditions. We also develop techniques to accelerate the TEDE and TEAE algorithms. Experiments on real-world public transport networks and traffic data demonstrate the effectiveness of our approach for MCJP. Experiment results also reveal interesting insights on the impact of the TOs, number of transfers, and number of travel criteria on MCJP algorithms, which can contribute to better public transportation planning. |
| first_indexed | 2024-10-01T02:39:26Z |
| format | Journal Article |
| id | ntu-10356/179466 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:39:26Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1794662024-08-05T05:41:10Z Exploring public transport transfer opportunities for Pareto search of multicriteria journeys He, Peilan Jiang, Guiyuan Lam, Siew-Kei Sun, Yidan Ning, Fangxin College of Computing and Data Science School of Computer Science and Engineering Computer and Information Science Journey planning Multimodal public transport network Multimodal public transport networks (MMPTNs) in modern cities are becoming increasingly complex. This makes finding optimal journey routes challenging due to a large number of transfer options that need to be properly considered. Furthermore, the complexity of the problem is compounded when multiple conflicting travel criteria are considered (e.g., travel time, walking distance, travel fare, etc.). This paper proposes a transfer graph (TG) model to explore the transfer opportunities of the MMPTN to support efficient journey route planning. TG considers all possible transfer opportunities, while employing a representative mechanism to optimize the TG structure that supports efficient route planning algorithms. Based on the proposed TG, we develop two exact algorithms to search the Pareto-optimal solutions for multi-criteria journey planning (MCJP) over the MMPTN. The first algorithm runs faster by eliminating many partial solutions at an early stage, which is more suited for lowering computation time at the expense of marginal degradation in output quality. In contrast, the second algorithm provides a more dependable solution by incorporating accurate journey time prediction that caters to the evolving traffic conditions. We also develop techniques to accelerate the TEDE and TEAE algorithms. Experiments on real-world public transport networks and traffic data demonstrate the effectiveness of our approach for MCJP. Experiment results also reveal interesting insights on the impact of the TOs, number of transfers, and number of travel criteria on MCJP algorithms, which can contribute to better public transportation planning. National Research Foundation (NRF) Submitted/Accepted version This work was supported in part by the National Research Foundation Singapore through the Campus for Research Excellence and Technological Enterprise (CREATE) Program, Technical University of Munich, TUMCREATE. 2024-08-05T05:41:10Z 2024-08-05T05:41:10Z 2022 Journal Article He, P., Jiang, G., Lam, S., Sun, Y. & Ning, F. (2022). Exploring public transport transfer opportunities for Pareto search of multicriteria journeys. IEEE Transactions On Intelligent Transportation Systems, 23(12), 22895-22908. https://dx.doi.org/10.1109/TITS.2022.3194523 1524-9050 https://hdl.handle.net/10356/179466 10.1109/TITS.2022.3194523 12 23 22895 22908 en IEEE Transactions on Intelligent Transportation Systems © 2022 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1109/TITS.2022.3194523. application/pdf |
| spellingShingle | Computer and Information Science Journey planning Multimodal public transport network He, Peilan Jiang, Guiyuan Lam, Siew-Kei Sun, Yidan Ning, Fangxin Exploring public transport transfer opportunities for Pareto search of multicriteria journeys |
| title | Exploring public transport transfer opportunities for Pareto search of multicriteria journeys |
| title_full | Exploring public transport transfer opportunities for Pareto search of multicriteria journeys |
| title_fullStr | Exploring public transport transfer opportunities for Pareto search of multicriteria journeys |
| title_full_unstemmed | Exploring public transport transfer opportunities for Pareto search of multicriteria journeys |
| title_short | Exploring public transport transfer opportunities for Pareto search of multicriteria journeys |
| title_sort | exploring public transport transfer opportunities for pareto search of multicriteria journeys |
| topic | Computer and Information Science Journey planning Multimodal public transport network |
| url | https://hdl.handle.net/10356/179466 |
| work_keys_str_mv | AT hepeilan exploringpublictransporttransferopportunitiesforparetosearchofmulticriteriajourneys AT jiangguiyuan exploringpublictransporttransferopportunitiesforparetosearchofmulticriteriajourneys AT lamsiewkei exploringpublictransporttransferopportunitiesforparetosearchofmulticriteriajourneys AT sunyidan exploringpublictransporttransferopportunitiesforparetosearchofmulticriteriajourneys AT ningfangxin exploringpublictransporttransferopportunitiesforparetosearchofmulticriteriajourneys |