# An enhanced iTransformer-based early warning system for predicting automotive rental contract breaches

## TL;DR

This paper introduces an early warning system using improved DBSCAN and iTransformer to predict and prevent customer breaches in the car rental industry, reducing economic losses.

## Contribution

The novel integration of an enhanced DBSCAN with a snow ablation optimizer and the iTransformer model for predicting rental contract breaches.

## Key findings

- The system accurately predicts vehicle resident location and future trajectory with low error metrics.
- The model achieves a mean square error of 0.001 and a location error of 0.08 kilometers.
- The approach effectively identifies and prevents customer default risks in real-time.

## Abstract

Economic losses in the car rental industry due to customer breaches remain a critical issue. The rapid growth of the vehicle leasing market has given rise to a pressing concern for enterprises, namely the economic loss, vehicle idleness, and service quality degradation that are often associated with customer default. This study proposes an innovative vehicle rental early warning system that incorporates the improved DBSCAN clustering technique and the iTransformer model. The enhanced DBSCAN technique, which employs a snow ablation optimizer (SAO) algorithm, establishes an electronic barrier and integrates the iTransformer model for trajectory prediction. This enables the real-time monitoring of potential customer defaults and the reduction of economic losses that leasing companies may incur as a result of customer defaults. The system identifies and prevents default risks in a timely manner through a comprehensive analysis of vehicle driving data, thereby safeguarding the interests of corporate entities. The system employs vehicle driving data provided by a Chinese company to accurately identify the vehicle’s resident location and predict future trajectory, effectively preventing customer defaults. The experimental results demonstrate that the model is highly effective in predicting the vehicle’s resident location and future trajectory. The mean square error (MSE), mean absolute error (MAE), and location error reached 0.001, 0.003, and 0.08 kilometers, respectively, which substantiates the model’s efficiency and accuracy. This study has the additional benefit of providing effective warnings to customers of potential default behavior, thereby reducing the economic losses incurred by enterprises. Such an approach not only ensures financial security but also enhances operational efficiency within the industry. Furthermore, it offers robust support for the sustainable development of the car rental industry.