Year: 2024
Type: conference-paper
Abstract: The swift expansion of the ride-hailing industry has given rise to pressing safety issues. This study investigates enhancing safety in the ride-hailing industry by developing an anomaly detection system for detecting abnormal vehicle trajectories. This system defines what constitutes an abnormal trajectory and establishes evaluation metrics for detection methods. Traditional methods for anomaly detection, such as real-time monitoring of speed and direction, are inadequate in the face of data heterogeneity and the sheer volume of information. And the proposed anomaly detection system is designed to overcome these limitations: We introduce a multi-tiered strategy, dividing the anomaly detection task into critical points (including origin and destination) analysis and path analyses to effectively identify and categorize abnormal patterns. By concentrating different features or patterns in each tier, the detection capability of this system is effectively enhanced. Combining our evaluation metrics, the system can provide risk assessment for abnormal critical points and trajectories, thus ensuring the safety of both drivers and passengers.
Authors: Sirui He, Chenye Wu, Zuxin Li, Yang Li
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405015090