Prediction of jaywalker-vehicle conflicts based on encoder-decoder framework utilizing multi-source data

Jaywalker-vehicle (J-V) conflicts at mid-blocks without crossing facilities in China are frequent and hazardous. Due to the unexpected and sudden nature of jaywalking activity, it is crucial to develop predictive models for J-V conflicts to offer pre-conflict warnings for road users. This study introduces a novel encoder-decoder framework that utilizes multi-source data to predict J-V conflict severity. We define three encoders to represent three types of input data, (1) J-V interaction encoder (Bi-LSTM), (2) jaywalker motion encoder (Bi-LSTM) and (3) back-ground information encoder (MLP). Subsequently, features extracted by these three encoders are concatenated and transferred to the conflict severity decoder (MLP) to obtain the predicted severity level.We further conduct a case study using the surveyed video data at three mid-blocks without crossing facilities in Nanjing, China. The experimental results indicate that, compared to classical models, our proposed encoder-decoder (Proposed ED) model exhibits the best and stable predictive metrics. Furthermore, the results of the ablation study suggest that the incorporation of background information significantly enhances the four evalu-ative metrics of the Proposed ED model, with an average improvement of 24.291%. Additionally, the results of transferability analysis suggest that, when the ratio of added samples from the new mid-block reaches 40% to 50%, the predictive metrics of the updated models could stabilize at around 80% to 95%, indicating a notably good performance. Eventually, we derive several practical suggestions from the above findings, in order to help with J-V conflict prediction and jaywalking safety improvement.

Automated summary

This study introduces a novel encoder-decoder framework that utilizes multi-source data to predict the severity of jaywalking violations. The experimental results show that the proposed model outperforms classical models and the incorporation of background information significantly enhances the model's performance.