期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2025-02-01 |
| 作品名稱 | Performance assessment of freeway HOV lanes via big data and deep learning |
| 作品名稱（其他語言） | 基於大數據與深度學習法之高速公路高乘載車輛車道績效評估 |
| 著者 | Chih-Lin Chung; Chia-Yi Pan |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | 台灣土地研究 27(1)，頁35-56 |
| 摘要 | High-occupancy vehicle (HOV) lanes provide incentives to encourage carpooling and prioritize public transit, but they usually face criticism for underutilization compared to general-purpose (GP) lanes. This research collected nine months of quantitative traffic data in 77,611 files from the Freeway Bureau's open database. Such data assessed utilization dynamics between HOV and GP lanes on the Wu-Yang Freeway Viaduct in northern Taiwan. The analysis revealed an overall balanced operational state. However, on certain consecutive holidays, specific HOV segments exhibited surplus capacity with levels of service (LOS) A to C, while GP lanes experienced severe congestion at LOS E to F, identifying lane management hot spots. A deep learning-based multilayer perceptron model was thus developed to predict congested segments and periods, achieving results within a reasonable margin of error and demonstrating practical feasibility. The research recommendations include (1) adjusting control measures for southbound HOV lanes south of the Airport Interchange to optimize lane usage, (2) enhancing carpooling environments to maximize the benefits of HOV infrastructure, and (3) integrating the open database's file and data formats to improve user-friendliness while incorporating additional traffic data for even better model accuracy. |
| 關鍵字 | Big Data; Deep Learning; Freeway Performance Assessment; High-Occupancy Vehicle (HOV) Lane; Multilayer Perceptron (MLP) Model |
| 語言 | en |
| ISSN | 1606-2554 |
| 期刊性質 | 國內 |
| 收錄於 | TSSCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129464 ) |