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

an hui wang

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 existing
work studies the problem of detecting the takeaway rider accidents based on food delivery trajectory data. In this
paper, 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 spatialtemporal sequence. In this paper, 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 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, due to 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 data set Geolife and a real-world Ele.me data set with over 3 million riders. The experimental
results show that the proposed model is superior to the state-of-the-art.