Event Traffic Forecasting with Sparse Multimodal Data

Xiao Han, Zhenduo zhang, Yiling Wu, Xinfeng Zhang, Zhe Wu

Abstract: With the development of deep learning, traffic forecasting technology has made significant progress and is being applied in many practical scenarios. However, various events held in cities, such as sporting events, exhibitions, concerts, etc., have a significant impact on traffic patterns of surrounding areas, causing current advanced prediction models to fail in this case. In this paper, to broaden the applicable scenarios of traffic forecasting, we focus on modeling the impact of events on traffic patterns and propose an event traffic forecasting problem with multimodal inputs. We outline the main challenges of this problem: diversity and sparsity of events, as well as insufficient data. To address these issues, we first use textual modal data containing rich semantics to describe the diverse characteristics of events. Then, we propose a simple yet effective multi-modal event traffic forecasting model that uses pre-trained text and traffic encoders to extract the embeddings and fuses the two embeddings for prediction. Encoders pre-trained on large-scale data have powerful generalization abilities to cope with the challenge of sparse data. Next, we design an efficient large language model-based event description text generation pipeline to build multi-modal event traffic forecasting datasets, ShenzhenCEC and SuzhouIEC. Experiments on two real-world datasets show that our method achieves state-of-the-art performance compared with eight baselines, reducing mean absolute error during the event peak period by 4.26\%. Code is available at: https://github.com/2448845600/EventTrafficForecasting.

Primary Subject Area: [Content] Multimodal Fusion

Secondary Subject Area: [Generation] Social Aspects of Generative AI

Relevance To Conference: To address the challenges of human events on transportation systems, we propose the event traffic forecasting problem for the first time. To solve this problem, we build a multimodal event traffic dataset containing text, time series, and graph modals. Then, we propose an event traffic prediction model to receive multimodal inputs for prediction. The task, data, and method of this study are all highly related to multimodality.

Submission Number: 1600