  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.
