ArrivalNet: Predicting City-wide Bus/Tram Arrival Time with Two-dimensional Temporal Variation Modeling
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Zirui, Wolf, Patrick, Wang, Meng |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Exploring Over-stationarization in Deep Learning-based Bus/Tram Arrival Time Prediction: Analysis and Non-stationary Effect Recovery
by: Li, Zirui, et al.
Published: (2025)
by: Li, Zirui, et al.
Published: (2025)
Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility
by: Rashvand, Narges, et al.
Published: (2023)
by: Rashvand, Narges, et al.
Published: (2023)
Less is More: Non-uniform Road Segments are Efficient for Bus Arrival Prediction
by: Huang, Zhen, et al.
Published: (2025)
by: Huang, Zhen, et al.
Published: (2025)
An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival
by: Schleibaum, Sören, et al.
Published: (2022)
by: Schleibaum, Sören, et al.
Published: (2022)
A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation
by: Kirchdorfer, Lukas, et al.
Published: (2025)
by: Kirchdorfer, Lukas, et al.
Published: (2025)
Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning
by: Zhang, Xiaocai, et al.
Published: (2024)
by: Zhang, Xiaocai, et al.
Published: (2024)
Data-Driven Fire Modeling: Learning First Arrival Times and Model Parameters with Neural Networks
by: Tong, Xin, et al.
Published: (2024)
by: Tong, Xin, et al.
Published: (2024)
A Large-Scale Analysis on the Use of Arrival Time Prediction for Automated Shuttle Services in the Real World
by: Schmidt, Carolin, et al.
Published: (2024)
by: Schmidt, Carolin, et al.
Published: (2024)
Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model
by: An, Lin, et al.
Published: (2024)
by: An, Lin, et al.
Published: (2024)
Direction of Arrival Estimation with Sparse Subarrays
by: Leite, W., et al.
Published: (2024)
by: Leite, W., et al.
Published: (2024)
Public Transit Arrival Prediction: a Seq2Seq RNN Approach
by: Bhutani, Nancy, et al.
Published: (2022)
by: Bhutani, Nancy, et al.
Published: (2022)
Learning an Inventory Control Policy with General Inventory Arrival Dynamics
by: Andaz, Sohrab, et al.
Published: (2023)
by: Andaz, Sohrab, et al.
Published: (2023)
Learning-Augmented Online Bipartite Matching in the Random Arrival Order Model
by: Burathep, Kunanon, et al.
Published: (2025)
by: Burathep, Kunanon, et al.
Published: (2025)
Enhanced Parcel Arrival Forecasting for Logistic Hubs: An Ensemble Deep Learning Approach
by: Pan, Xinyue, et al.
Published: (2026)
by: Pan, Xinyue, et al.
Published: (2026)
Arrival Cities
Published: (2020)
Published: (2020)
SABER: Symbolic Regression-based Angle of Arrival and Beam Pattern Estimator
by: Chou, Shih-Kai, et al.
Published: (2025)
by: Chou, Shih-Kai, et al.
Published: (2025)
A Learning-Based Superposition Operator for Non-Renewal Arrival Processes in Queueing Networks
by: Sherzer, Eliran
Published: (2026)
by: Sherzer, Eliran
Published: (2026)
Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals
by: Liu, Junyan, et al.
Published: (2025)
by: Liu, Junyan, et al.
Published: (2025)
Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition
by: Orfanidis, Georgios I., et al.
Published: (2026)
by: Orfanidis, Georgios I., et al.
Published: (2026)
Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design
by: Chang, Zhan-Lun, et al.
Published: (2024)
by: Chang, Zhan-Lun, et al.
Published: (2024)
Poisson-MNL Bandit: Nearly Optimal Dynamic Joint Assortment and Pricing with Decision-Dependent Customer Arrivals
by: Cai, Junhui, et al.
Published: (2026)
by: Cai, Junhui, et al.
Published: (2026)
Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation
by: Orfanidis, Georgios I.
Published: (2026)
by: Orfanidis, Georgios I.
Published: (2026)
Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT Public Bus Transit
by: Rashvand, Narges, et al.
Published: (2025)
by: Rashvand, Narges, et al.
Published: (2025)
Time Difference of Arrival Extraction from Two-Way Ranging
by: Rathje, Patrick, et al.
Published: (2022)
by: Rathje, Patrick, et al.
Published: (2022)
A Comparative Study of Invariance-Aware Loss Functions for Deep Learning-based Gridless Direction-of-Arrival Estimation
by: Chen, Kuan-Lin, et al.
Published: (2025)
by: Chen, Kuan-Lin, et al.
Published: (2025)
Bayesian Deep Learning Approach for Real-time Lane-based Arrival Curve Reconstruction at Intersection using License Plate Recognition Data
by: He, Yang, et al.
Published: (2024)
by: He, Yang, et al.
Published: (2024)
Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach
by: Nie, Tong, et al.
Published: (2024)
by: Nie, Tong, et al.
Published: (2024)
A Hybrid Game-Theory and Deep Learning Framework for Predicting Tourist Arrivals via Big Data Analytics and Opinion Leader Detection
by: Nikseresht, Ali
Published: (2025)
by: Nikseresht, Ali
Published: (2025)
InvDec: Inverted Decoder for Multivariate Time Series Forecasting with Separated Temporal and Variate Modeling
by: Wang, Yuhang
Published: (2025)
by: Wang, Yuhang
Published: (2025)
Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction
by: Azenkot, Daniel, et al.
Published: (2026)
by: Azenkot, Daniel, et al.
Published: (2026)
Bus Arrival Time Prediction Based on the Optimized Long Short‐Term Memory Neural Network Model With the Improved Whale Algorithm
by: Bing Zhang, et al.
Published: (2024)
by: Bing Zhang, et al.
Published: (2024)
Met$^2$Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for Complex Meteorological Systems
by: Li, Shaohan, et al.
Published: (2025)
by: Li, Shaohan, et al.
Published: (2025)
OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction
by: Li, Zhonghang, et al.
Published: (2024)
by: Li, Zhonghang, et al.
Published: (2024)
GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models through Statistically-Guided Geo-Prototyping
by: An, Bang, et al.
Published: (2024)
by: An, Bang, et al.
Published: (2024)
Explain Variance of Prediction in Variational Time Series Models for Clinical Deterioration Prediction
by: Liu, Jiacheng, et al.
Published: (2024)
by: Liu, Jiacheng, et al.
Published: (2024)
DynaSTy: A Framework for SpatioTemporal Node Attribute Prediction in Dynamic Graphs
by: Banerji, Namrata, et al.
Published: (2026)
by: Banerji, Namrata, et al.
Published: (2026)
Food Delivery Time Prediction in Indian Cities Using Machine Learning Models
by: Garg, Ananya, et al.
Published: (2025)
by: Garg, Ananya, et al.
Published: (2025)
LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction
by: Jiang, Jiawei, et al.
Published: (2023)
by: Jiang, Jiawei, et al.
Published: (2023)
Diffeomorphic Temporal Alignment Nets for Time-series Joint Alignment and Averaging
by: Weber, Ron Shapira, et al.
Published: (2025)
by: Weber, Ron Shapira, et al.
Published: (2025)
TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification
by: Liu, Huaiyuan, et al.
Published: (2023)
by: Liu, Huaiyuan, et al.
Published: (2023)
Similar Items
-
Exploring Over-stationarization in Deep Learning-based Bus/Tram Arrival Time Prediction: Analysis and Non-stationary Effect Recovery
by: Li, Zirui, et al.
Published: (2025) -
Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility
by: Rashvand, Narges, et al.
Published: (2023) -
Less is More: Non-uniform Road Segments are Efficient for Bus Arrival Prediction
by: Huang, Zhen, et al.
Published: (2025) -
An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival
by: Schleibaum, Sören, et al.
Published: (2022) -
A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation
by: Kirchdorfer, Lukas, et al.
Published: (2025)