How to Eliminate Detour Behaviors in E-hailing? Real-time Detecting and Time-dependent Pricing
This addresses a traditional fraud issue in taxi services to improve passenger trust and service quality, though it is incremental as it builds on existing detection methods with new pricing insights.
The paper tackles the problem of greedy taxi drivers taking unnecessary detours to overcharge passengers in e-hailing services by proposing a framework for real-time detection and time-dependent pricing, achieving high performance with AUC surpassing 0.98 in offline classification and 0.90 in online detection, and providing quantitative pricing suggestions to reduce detours.
With the rapid development of information and communication technology (ICT), taxi business becomes a typical electronic commerce mode. However, one traditional problem still exists in taxi service, that greedy taxi drivers may deliberately take unnecessary detours to overcharge passengers. The detection of these fraudulent behaviors is essential to ensure high-quality taxi service. In this paper, we propose a novel framework for detecting and analyzing the detour behaviors both in off-line database and among on-line trips. Applying our framework to real-world taxi data-set, a remarkable performance (AUC surpasses 0.98) has been achieved in off-line classification. Meanwhile, we further extend the off-line methods to on-line detection, a warning mechanism is introduced to remind drivers and an excellent precision (AUC surpasses 0.90) also has arrived in this phases. After conducting extensive experiments to verify the relationships between pricing regulations and detour behaviors, some quantitative pricing suggestions, including rising base fare and reducing distance-based fare rate, are provided to eliminate detour behaviors from the long term.