Exploring Intercity Trip Patterns of Railway Systems on National Holidays Using Deep Auto-Encoder
| Autor: | Wen-Yu Lee, Chian-Shan Suen, Ying-Chuan Ni, Ming-Hsuan Wu, Yu-Ting Hsu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Transport engineering
050210 logistics & transportation Railway system Computer science Mechanical Engineering 0502 economics and business 05 social sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Autoencoder Civil and Structural Engineering |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2674:662-672 |
| ISSN: | 2169-4052 0361-1981 |
| DOI: | 10.1177/0361198120917385 |
| Popis: | Intercity railway system operation on national holidays can be challenging because of possible surging demand. This study proposes an analysis framework to investigate railway system ridership data on national holidays, seeking to attain better understanding of relevant intercity trip patterns, so as to enable enhanced preparation and response before and during national holidays. The ridership data are analyzed in the form of Origin–Destination (O-D) tables and regarded as pictures of N × N pixels, where N is the number of the considered stations/cities in a railway system. The framework primarily adopts a deep auto-encoder to process these pictures to reduce data dimensions and abstracting key features within these pictorial data. Based on the abstracted features, k-means clustering is then conducted to categorize the O-D tables with similar trip patterns into the same group. Further, a discrete outcome model based on logistic regression is developed on the clustering results to enhance the interpretation of the trip pattern in each group and identify the significant holiday-related characteristics and external factors that can affect the trip pattern generation. The ridership data of Taiwan Railways Administration associated with 38 national holidays from January 2014 to August 2018 are analyzed. The analysis results highlight insightful interpretation in relation to clustered trip patterns and relevant trip characterization relative to various national holidays. The proposed framework and developed discrete outcome model are also validated, showing 85% correct assignments of O-D tables to the groups of relevant trip patterns. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|