# Deep Sequence Learning with Auxiliary Information for Traffic Prediction

arXiv: https://arxiv.org/abs/1806.07380 · HTML: https://mlsanity.com/paper/1806.07380/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 30 of 30)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models](https://mlsanity.com/paper/2210.02447/) (2022)
- [LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction](https://mlsanity.com/paper/2304.14343/) (2023)
- [Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction](https://mlsanity.com/paper/2108.00238/) (2021)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation](https://mlsanity.com/paper/2107.00946/) (2021)
- [Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph](https://mlsanity.com/paper/2101.00752/) (2021)
- [Adversarial Learning for Incentive Optimization in Mobile Payment Marketing](https://mlsanity.com/paper/2112.15434/) (2021)
- [Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools](https://mlsanity.com/paper/2103.11824/) (2021)
- [Counterfactual Explanations for Deep Learning-Based Traffic Forecasting](https://mlsanity.com/paper/2405.00456/) (2024)
- [MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecasting](https://mlsanity.com/paper/2108.03594/) (2021)
- [Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting](https://mlsanity.com/paper/2206.11078/) (2022)
- [AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting](https://mlsanity.com/paper/2209.03356/) (2022)
- [Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities](https://mlsanity.com/paper/2302.08761/) (2023)
- [Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]](https://mlsanity.com/paper/2308.12899/) (2023)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [TapNet: The Design, Training, Implementation, and Applications of a Multi-Task Learning CNN for Off-Screen Mobile Input](https://mlsanity.com/paper/2102.09087/) (2021)
- [PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction](https://mlsanity.com/paper/2108.02424/) (2021)
- [A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles](https://mlsanity.com/paper/2201.04968/) (2022)
- [Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Masking](https://mlsanity.com/paper/2301.04233/) (2023)
- [STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models](https://mlsanity.com/paper/2501.03583/) (2025)
- [RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models](https://mlsanity.com/paper/2607.06504/) (2026)
- [Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency](https://mlsanity.com/paper/2104.09083/) (2021)
- [CityNet: A Comprehensive Multi-Modal Urban Dataset for Advanced Research in Urban Computing](https://mlsanity.com/paper/2106.15802/) (2021)
- [Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction](https://mlsanity.com/paper/2308.05601/) (2023)
- [Transport-Hub-Aware Spatial-Temporal Adaptive Graph Transformer for Traffic Flow Prediction](https://mlsanity.com/paper/2310.08328/) (2023)
- [Informed along the road: roadway capacity driven graph convolution network for network-wide traffic prediction](https://mlsanity.com/paper/2406.13057/) (2024)
- [Graph Based Traffic Analysis and Delay Prediction](https://mlsanity.com/paper/2410.21028/) (2024)
- [Joint upper-bound coverage and route-choice utility: an empirical evaluation on two urban proxy tasks](https://mlsanity.com/paper/2610.06995/) (2026)

## Abstract (arXiv)

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework.