[Paper Review] TRAILER: Transformer-based Time-wise Long Term Relation Modeling for Citywide Traffic Flow Prediction.

Hao Xue, Flora D. Salim|arXiv (Cornell University)|Nov 11, 2020

Traffic Prediction and Management Techniques12 references2 citations

TL;DR

TRAILER proposes a Transformer-based architecture to model long-term temporal relations in citywide traffic flow prediction by explicitly capturing periodic patterns through a dedicated long-term relation module and enforcing consistency between predicted relations and traffic flows via a consistency module and loss. It achieves state-of-the-art performance on three real-world datasets.

ABSTRACT

Traffic flow prediction is a crucial task in enabling efficient intelligent transportation systems and smart cities. Although there has been rapid progress in this area in the last few years, given the major advances of deep learning techniques, it remains a challenging task because of the inherent periodic characteristics of traffic flow sequence. To incorporate the periodicity in the prediction process, existing methods have observed three components separately as the input of prediction models, i.e., the closeness, period, and trend components. The long term relation of these components has not been fully addressed. In this paper, we present a novel architecture, TRAILER (TRAnsformer-based tIme-wise Long tErm Relation modeling), to predict traffic flows more effectively. First, we explicitly design a Transformer based long term relation prediction module to model the long term relation and predict the periodic relation to be used for the downstream task. Second, we propose a consistency module at the target time interval, in order to model the consistency of the predicted periodic relation and the relation inferred from the predicted traffic flow tensor. Finally, based on the consistency module, we introduce a consistency loss to stabilize the training process and further improve the prediction performance. Through extensive experiments, we show the superiority of the proposed method on three real-world datasets and the effectiveness of each module in TRAILER.

Motivation & Objective

• To address the challenge of modeling long-term temporal dependencies in traffic flow sequences, particularly their periodic characteristics.
• To explicitly capture and predict periodic relations across closeness, period, and trend components in traffic data.
• To improve training stability and prediction accuracy by enforcing consistency between predicted periodic relations and actual traffic flow predictions.
• To develop a unified framework that integrates long-term relation modeling with end-to-end traffic flow forecasting.

Proposed method

• Introduces a Transformer-based long-term relation prediction module to model temporal dependencies across multiple time scales.
• Designs a consistency module that aligns predicted periodic relations with the relations derived from the predicted traffic flow tensor at the target time interval.
• Proposes a consistency loss that penalizes discrepancies between the predicted periodic relation and the relation inferred from the predicted flow, stabilizing training.
• Processes traffic sequences by decomposing them into closeness, period, and trend components before feeding them into the TRAILER architecture.
• Leverages self-attention mechanisms in the Transformer to capture long-range dependencies across extended temporal horizons.
• End-to-end training is performed with the consistency loss to jointly optimize relation modeling and traffic flow prediction.

Experimental results

Research questions

• RQ1 How can long-term temporal relations in traffic flow sequences be effectively modeled to improve prediction accuracy?
• RQ2 To what extent does enforcing consistency between predicted periodic relations and inferred relations from predicted flows enhance model stability and performance?
• RQ3 Can a dedicated Transformer-based module for long-term relation learning outperform conventional methods in capturing periodic patterns in traffic data?
• RQ4 How does the consistency loss contribute to training convergence and generalization in traffic flow prediction?

Key findings

• TRAILER achieves state-of-the-art performance on three real-world traffic flow prediction datasets, demonstrating superior accuracy over existing methods.
• The ablation study confirms that both the long-term relation module and the consistency module significantly improve prediction performance.
• The consistency loss leads to more stable training dynamics and reduces prediction variance across different time intervals.
• The model effectively captures long-term periodic patterns, especially in sequences with complex, multi-scale temporal dependencies.
• The integration of explicit periodic relation modeling improves generalization, particularly in long-horizon forecasting scenarios.