Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram
Transportation simulation and analysis projects that utilize maps with inappropriate fidelity levels carry a significant risk of having poor runtime or poor prediction performance. To address this, researchers use map abstraction method to abstract out a simplified map with fewer links and nodes bas...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/9540386 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563859806748672 |
|---|---|
| author | Yudi Li Lei Zhu Jian Sun Ye Tian |
| author_facet | Yudi Li Lei Zhu Jian Sun Ye Tian |
| author_sort | Yudi Li |
| collection | DOAJ |
| description | Transportation simulation and analysis projects that utilize maps with inappropriate fidelity levels carry a significant risk of having poor runtime or poor prediction performance. To address this, researchers use map abstraction method to abstract out a simplified map with fewer links and nodes based on the original full detailed map. Traditional static abstraction methods produce analysis maps with a single fidelity across the entire planning horizon, which cannot reflect the dynamic changes of daily traffic. This paper proposes a spatiotemporal dynamic map abstraction approach that adopts a time series clustering method to segment the analysis time horizon adaptively based on a Macroscopic Fundamental Diagram (MFD) curve, which describes network-wide dynamic traffic states. Time periods with similar macro-performance are grouped into one subinterval. A map with a dedicated fidelity is produced for each subinterval. Furthermore, a simulation is run on multiple abstracted maps with different fidelities in a sequence according to their temporal order. A numerical experiment ascertains that the proposed approach has promising results in both analysis accuracy and efficiency for resource-constrained modeling agents. |
| format | Article |
| id | doaj-art-a6a393ef48814ee5a0f574c4ff140f81 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a6a393ef48814ee5a0f574c4ff140f812025-02-03T01:12:27ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/95403869540386Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental DiagramYudi Li0Lei Zhu1Jian Sun2Ye Tian3Key Laboratory of Road and Traffic Engineering in the Ministry of Education, Tongji University, Shanghai, 201804, ChinaDepartment of Civil and Architectural Engineering and Mechanics, University of Arizona, Tucson, AZ, 85721, USAKey Laboratory of Road and Traffic Engineering in the Ministry of Education, Tongji University, Shanghai, 201804, ChinaKey Laboratory of Road and Traffic Engineering in the Ministry of Education, Tongji University, Shanghai, 201804, ChinaTransportation simulation and analysis projects that utilize maps with inappropriate fidelity levels carry a significant risk of having poor runtime or poor prediction performance. To address this, researchers use map abstraction method to abstract out a simplified map with fewer links and nodes based on the original full detailed map. Traditional static abstraction methods produce analysis maps with a single fidelity across the entire planning horizon, which cannot reflect the dynamic changes of daily traffic. This paper proposes a spatiotemporal dynamic map abstraction approach that adopts a time series clustering method to segment the analysis time horizon adaptively based on a Macroscopic Fundamental Diagram (MFD) curve, which describes network-wide dynamic traffic states. Time periods with similar macro-performance are grouped into one subinterval. A map with a dedicated fidelity is produced for each subinterval. Furthermore, a simulation is run on multiple abstracted maps with different fidelities in a sequence according to their temporal order. A numerical experiment ascertains that the proposed approach has promising results in both analysis accuracy and efficiency for resource-constrained modeling agents.http://dx.doi.org/10.1155/2019/9540386 |
| spellingShingle | Yudi Li Lei Zhu Jian Sun Ye Tian Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram Journal of Advanced Transportation |
| title | Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram |
| title_full | Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram |
| title_fullStr | Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram |
| title_full_unstemmed | Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram |
| title_short | Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram |
| title_sort | generating a spatiotemporal dynamic map for traffic analysis using macroscopic fundamental diagram |
| url | http://dx.doi.org/10.1155/2019/9540386 |
| work_keys_str_mv | AT yudili generatingaspatiotemporaldynamicmapfortrafficanalysisusingmacroscopicfundamentaldiagram AT leizhu generatingaspatiotemporaldynamicmapfortrafficanalysisusingmacroscopicfundamentaldiagram AT jiansun generatingaspatiotemporaldynamicmapfortrafficanalysisusingmacroscopicfundamentaldiagram AT yetian generatingaspatiotemporaldynamicmapfortrafficanalysisusingmacroscopicfundamentaldiagram |