Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model
This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/7905609 |
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| _version_ | 1832550897967693824 |
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| author | Christina Ng Susilawati Susilawati Md Abdus Samad Kamal Irene Mei Leng Chew |
| author_facet | Christina Ng Susilawati Susilawati Md Abdus Samad Kamal Irene Mei Leng Chew |
| author_sort | Christina Ng |
| collection | DOAJ |
| description | This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average). |
| format | Article |
| id | doaj-art-4394ddf4d8ab46f98e4485512f8017b8 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-4394ddf4d8ab46f98e4485512f8017b82025-02-03T06:05:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/79056097905609Development of Macroscopic Cell-Based Logistic Lane Change Prediction ModelChristina Ng0Susilawati Susilawati1Md Abdus Samad Kamal2Irene Mei Leng Chew3School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, MalaysiaSchool of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, MalaysiaDivision of Mechanical Science and Technology, Graduate School of Science and Technology, Gunma University, 1-5-1 Tenjincho, Kiryu 376-8515, JapanSchool of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, MalaysiaThis paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).http://dx.doi.org/10.1155/2021/7905609 |
| spellingShingle | Christina Ng Susilawati Susilawati Md Abdus Samad Kamal Irene Mei Leng Chew Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model Journal of Advanced Transportation |
| title | Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model |
| title_full | Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model |
| title_fullStr | Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model |
| title_full_unstemmed | Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model |
| title_short | Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model |
| title_sort | development of macroscopic cell based logistic lane change prediction model |
| url | http://dx.doi.org/10.1155/2021/7905609 |
| work_keys_str_mv | AT christinang developmentofmacroscopiccellbasedlogisticlanechangepredictionmodel AT susilawatisusilawati developmentofmacroscopiccellbasedlogisticlanechangepredictionmodel AT mdabdussamadkamal developmentofmacroscopiccellbasedlogisticlanechangepredictionmodel AT irenemeilengchew developmentofmacroscopiccellbasedlogisticlanechangepredictionmodel |