Risk prediction on traffic accidents using a compact neural model for multimodal information fusion over urban big data
Title
Risk prediction on traffic accidents using a compact neural model for multimodal information fusion over urban big data
Subject
Forecasting
Big data
Maps
Roads and streets
Data mining
Highway planning
Automobile drivers
Highway accidents
Taxicabs
Description
Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response. Here, the challenge lies in the multimodal nature of urban big data. We propose a compact neural ensemble model to alleviate overfitting in fusing multimodal features and develop some new features such as fractal measure of road complexity in satellite images, taxi flows, POIs, and road width and connectivity in OpenStreetMap. The solution is more promising in performance than the baseline methods and the single-modality data based solutions. After visualization from a micro view, the visual patterns of the scenes related to high and low risk are revealed, providing lessons for future road design. From city point of view, the predicted risk map is close to the ground truth, and can act as the base in optimizing spatial configuration of resources for emergency response, and alarming signs. To the best of our knowledge, it is the first work to fuse visual and spatio-temporal features in traffic accident prediction while advances to bridge the gap between data mining based urban computing and computer vision based urban perception. Copyright 2021, The Authors. All rights reserved.
Creator
Wang, Wenshan
Yang, Su
Zhang, Weishan
Date
2021
Type
journalArticle
Identifier
23318422
Citation
Wang, Wenshan, Yang, Su, and Zhang, Weishan, “Risk prediction on traffic accidents using a compact neural model for multimodal information fusion over urban big data,” Lamar University Midstream Center Research, accessed May 4, 2024, https://lumc.omeka.net/items/show/29403.