A bus origin-destination prediction model based on deep spatio-temporal residual networks
Incorporating built environment features
Bibliographic Data
| ID | 21602586 |
|---|---|
| Authors | Xinghua Hu (0000-0002-5593-8894, Chongqing Institute of Green and Intelligent Technology), Na Yang (0000-0002-7173-9457, Chongqing Jiaotong University), Jiahao Zhao (0000-0003-4144-3230, Chongqing Institute of Green and Intelligent Technology, corresponding author), Zhouzuo Wang (Chongqing Jiaotong University), Nanhao Wang (Chongqing Jiaotong University) |
| Year | 2026 |
| Volume | 134 |
| Pages | 104683 |
| Publication date | 2026-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Transport Geography (JOURNAL) |
| Journal identifiers | ISSN: 0966-6923 • E-ISSN: 1873-1236 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.jtrangeo.2026.104683 |
| OpenAlex | W7155095793 |
| Language | EN |
| References cited | 41 |
Built environment · Convolutional neural network · Population · Public transport · Residual · Human Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Transportation Planning and Optimization
Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction
Exploring the complex relationship between metro and shared bikes in the built environment
Spatiotemporal heterogeneity exploration in the effects of TOD structural characteristics on metro ridership
How urban density, network topology and socio-economy influence public transport ridership
Examining the spatial-temporal relationship between urban built environment and taxi ridership
A novel multimodal deep learning-based direct ridership model for planning-oriented demand prediction in urban rail transit networks
Heterogeneous impacts of the built environment on survival of customized bus services across the downtown and suburbs
Examining the nonlinear and interactive effects of built environment characteristics on travel satisfaction
Changes in car and bus usage amid the Covid-19 pandemic
Understanding the effects of complex seasonality on suburban daily transit ridership
| Citation velocity | historical |
|---|---|
| Highly cited | No |