Analysis of driving styles in cut-in behaviors on highway segments using trajectory data
Vehicle Platoons (VPs) organize multiple vehicles to travel together, forming an efficient and coordinated moving group. For the foreseeable future, VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flows, with frequent instances of VPs cutting into the trajectories of HDVs. To effe...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/180063 |
| _version_ | 1811683767652188160 |
|---|---|
| author | Hou, Qingqing |
| author2 | Su Rong |
| author_facet | Su Rong Hou, Qingqing |
| author_sort | Hou, Qingqing |
| collection | NTU |
| description | Vehicle Platoons (VPs) organize multiple vehicles to travel together, forming an efficient and coordinated moving group. For the foreseeable future, VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flows, with frequent instances of VPs cutting into the trajectories of HDVs. To effectively understand and manage this cut-in behavior, it is crucial to grasp the underlying driving styles. Based on real-world natural vehicle trajectory data, this study explores the classification and analysis of driving styles during the cut-in process in vehicles, especially on highway segments. The research initially preprocesses a real-world natural dataset, including wavelet denoising. Fourteen features are selected from the speed-change preparation phase and lane-change phase, covering the state of the vehicle itself and its interaction with other vehicles. Next, Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are used to reduce the dimensionality, followed by clustering driving styles using the K-means++ algorithm. The results show that for the selected features, t-SNE technique shows better clustering effect than PCA as a whole, with optimal clustering performance at four clusters. Therefore, we categorize the driving styles of highway segment vehicle cut-in into four types. Finally, the characteristics of cut-in process of different driving styles are analyzed statistically, which shows that it is effective to classify driving styles based on real natural data set. |
| first_indexed | 2024-10-01T04:17:58Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/180063 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:17:58Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1800632024-09-13T15:44:23Z Analysis of driving styles in cut-in behaviors on highway segments using trajectory data Hou, Qingqing Su Rong School of Electrical and Electronic Engineering RSu@ntu.edu.sg Engineering Cut-in Driving style Dimensionality reduction Clustering Vehicle Platoons (VPs) organize multiple vehicles to travel together, forming an efficient and coordinated moving group. For the foreseeable future, VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flows, with frequent instances of VPs cutting into the trajectories of HDVs. To effectively understand and manage this cut-in behavior, it is crucial to grasp the underlying driving styles. Based on real-world natural vehicle trajectory data, this study explores the classification and analysis of driving styles during the cut-in process in vehicles, especially on highway segments. The research initially preprocesses a real-world natural dataset, including wavelet denoising. Fourteen features are selected from the speed-change preparation phase and lane-change phase, covering the state of the vehicle itself and its interaction with other vehicles. Next, Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are used to reduce the dimensionality, followed by clustering driving styles using the K-means++ algorithm. The results show that for the selected features, t-SNE technique shows better clustering effect than PCA as a whole, with optimal clustering performance at four clusters. Therefore, we categorize the driving styles of highway segment vehicle cut-in into four types. Finally, the characteristics of cut-in process of different driving styles are analyzed statistically, which shows that it is effective to classify driving styles based on real natural data set. Master's degree 2024-09-12T00:55:56Z 2024-09-12T00:55:56Z 2024 Thesis-Master by Coursework Hou, Q. (2024). Analysis of driving styles in cut-in behaviors on highway segments using trajectory data. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/180063 https://hdl.handle.net/10356/180063 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Cut-in Driving style Dimensionality reduction Clustering Hou, Qingqing Analysis of driving styles in cut-in behaviors on highway segments using trajectory data |
| title | Analysis of driving styles in cut-in behaviors on highway segments using trajectory data |
| title_full | Analysis of driving styles in cut-in behaviors on highway segments using trajectory data |
| title_fullStr | Analysis of driving styles in cut-in behaviors on highway segments using trajectory data |
| title_full_unstemmed | Analysis of driving styles in cut-in behaviors on highway segments using trajectory data |
| title_short | Analysis of driving styles in cut-in behaviors on highway segments using trajectory data |
| title_sort | analysis of driving styles in cut in behaviors on highway segments using trajectory data |
| topic | Engineering Cut-in Driving style Dimensionality reduction Clustering |
| url | https://hdl.handle.net/10356/180063 |
| work_keys_str_mv | AT houqingqing analysisofdrivingstylesincutinbehaviorsonhighwaysegmentsusingtrajectorydata |