# Attention-based Supply-Demand Prediction for Autonomous Vehicles

## TL;DR

This paper introduces two attention-based models for predicting supply and demand of autonomous vehicles, leveraging spatial, temporal, and semantic data to improve accuracy in different data availability scenarios.

## Contribution

The paper presents novel ARLP and Advanced ARLP models that integrate residual networks, LSTM, and multi-attention mechanisms for comprehensive supply-demand prediction.

## Key findings

- Models outperform existing methods in accuracy and stability.
- Effective integration of spatial, temporal, and semantic relations.
- Models adapt to different data availability scenarios.

## Abstract

As one of the important functions of the intelligent transportation system (ITS), supply-demand prediction for autonomous vehicles provides a decision basis for its control. In this paper, we present two prediction models (i.e. ARLP model and Advanced ARLP model) based on two system environments that only the current day's historical data is available or several days' historical data are available. These two models jointly consider the spatial, temporal, and semantic relations. Spatial dependency is captured with residual network and dimension reduction. Short term temporal dependency is captured with LSTM. Long term temporal dependency and temporal shifting are captured with LSTM and attention mechanism. Semantic dependency is captured with multi-attention mechanism and autocorrelation coefficient method. Extensive experiments show that our frameworks provide more accurate and stable prediction results than the existing methods.