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TARA-Net: A Fusion Network for Detecting Takeaway Rider Accidents

Authors:

Ting YuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 12, Issue 6

Article No.: 72, Pages 1 - 19

https://doi.org/10.1145/3457218

Published: 11 December 2021 Publication History

Abstract

In the emerging business of food delivery, rider traffic accidents raise financial cost and social traffic burden. Although there has been much effort on traffic accident forecasting using temporal-spatial prediction models, none of the existing work studies the problem of detecting the takeaway rider accidents based on food delivery trajectory data. In this article, we aim to detect whether a takeaway rider meets an accident on a certain time period based on trajectories of food delivery and riders’ contextual information. The food delivery data has a heterogeneous information structure and carries contextual information such as weather and delivery history, and trajectory data are collected as a spatial-temporal sequence. In this article, we propose a TakeAway Rider Accident detection fusion network TARA-Net to jointly model these heterogeneous and spatial-temporal sequence data. We utilize the residual network to extract basic contextual information features and take advantage of a transformer encoder to capture trajectory features. These embedding features are concatenated into a pyramidal feed-forward neural network. We jointly train the above three components to combine the benefits of spatial-temporal trajectory data and sparse basic contextual data for early detecting traffic accidents. Furthermore, although traffic accidents rarely happen in food delivery, we propose a sampling mechanism to alleviate the imbalance of samples when training the model. We evaluate the model on a transportation mode classification dataset Geolife and a real-world Ele.me dataset with over 3 million riders. The experimental results show that the proposed model is superior to the state-of-the-art.

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Cited By

Gu YCho EOh CLee G(2025)Riding safety Evaluation of food delivery motor scooters based on Associating Sensor-based riding behavior and road traffic characteristicsAccident Analysis & Prevention10.1016/j.aap.2024.107871211(107871)Online publication date: Mar-2025
https://doi.org/10.1016/j.aap.2024.107871
Bhardwaj NPal ABhumika Das D(2024)Adaptive Context Based Road Accident Risk Prediction Using Spatio-Temporal Deep LearningIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33285785:6(2872-2883)Online publication date: Jun-2024
https://doi.org/10.1109/TAI.2023.3328578
Lin CZhang SGong BLiu H(2024)Near-crash risk identification and evaluation for takeout delivery motorcycles using roadside LiDARAccident Analysis & Prevention10.1016/j.aap.2024.107520199(107520)Online publication date: May-2024
https://doi.org/10.1016/j.aap.2024.107520
Show More Cited By

Index Terms

TARA-Net: A Fusion Network for Detecting Takeaway Rider Accidents
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Learning latent representations
      2. Neural networks
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 12, Issue 6

December 2021

356 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3501281

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 11 December 2021

Accepted: 01 March 2021

Revised: 01 February 2021

Received: 01 October 2020

Published in TIST Volume 12, Issue 6

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Cited By

Gu YCho EOh CLee G(2025)Riding safety Evaluation of food delivery motor scooters based on Associating Sensor-based riding behavior and road traffic characteristicsAccident Analysis & Prevention10.1016/j.aap.2024.107871211(107871)Online publication date: Mar-2025
https://doi.org/10.1016/j.aap.2024.107871
Bhardwaj NPal ABhumika Das D(2024)Adaptive Context Based Road Accident Risk Prediction Using Spatio-Temporal Deep LearningIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33285785:6(2872-2883)Online publication date: Jun-2024
https://doi.org/10.1109/TAI.2023.3328578
Lin CZhang SGong BLiu H(2024)Near-crash risk identification and evaluation for takeout delivery motorcycles using roadside LiDARAccident Analysis & Prevention10.1016/j.aap.2024.107520199(107520)Online publication date: May-2024
https://doi.org/10.1016/j.aap.2024.107520
Lin CZhang SGong BLiu HSun G(2023)Identification and Tracking of Takeout Delivery Motorcycles Using Low-Channel Roadside LiDARIEEE Sensors Journal10.1109/JSEN.2023.326329823:9(9786-9795)Online publication date: 1-May-2023
https://doi.org/10.1109/JSEN.2023.3263298

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