Time Granularity Setting Principle for Short-Term Passenger Flow Prediction in Urban Rail Transit
Bibliographic Data
| ID | 22107128 |
|---|---|
| Authors | Guangyu Zhu (0000-0002-8641-2422, Beijing Jiaotong University), Yansu Gong (0009-0006-1037-3144, Beijing Jiaotong University), Jiacun Ding (0000-0002-0474-3894, Beijing Jiaotong University), Edmond Q Wu (0000-0003-1301-9870, Shanghai Jiao Tong University), Robin Law (0000-0001-7199-3757, University of Macau) |
| Year | 2024 |
| Volume | 11 |
| Issue | 5 |
| Pages | 6466-6475 |
| Publication date | 2024-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3385850 |
| OpenAlex | W4396782865 |
| Language | EN |
| References cited | 28 |
Time granularity is a key parameter necessary for short-time passenger flow prediction of urban rail transit (URT); however, no universal method is available for its setting. This study investigates the time granularity setting principle for short-term passenger flow prediction in URT. First, a method to measure the autocorrelation of passenger flow time series is constructed, focusing on the comparison of time granularities. Second, based on the functional relationship between the first-order autocorrelation coefficients of the passenger flow time series under different time granularities, the time granularity setup principle is obtained for different passenger flow characteristics. Finally, the reasonableness and universality of the time granularity setting principle are verified by analyzing the passenger flow characteristics and autocorrelation magnitude of the actual inbound and origin-destination (OD) passenger flow data under different stations and dates at different time granularities
Granularity · Physics · Rail transit · Rail transportation · Transport engineering · Urban rail transit · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Mathematics · Traffic Prediction and Management Techniques · Transportation Planning and Optimization
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| Highly cited | No |
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