Understanding detour behavior in taxi services: A combined approach

Taxi is one of the most important ways for citizens' daily travel, but taxi service faces a typical problem that greedy drivers may deliberately take unnecessary detours to overcharge passengers. An in-depth analysis of drivers' detour behavior is necessary to ensure high-quality service. In this paper, two kinds of detour patterns, namely kind detours and malicious detours, are defined and identified based on taxi datasets collected from three metropolitan cities in two countries. To better understand the detour choices of drivers, we explore the factors that may influence different detour patterns in terms of drivers, spatio-temporal distribution, land use, and network characteristics, and find that these two types of detours have distinctly different features. Based on these analyses, the detour behavior is modeled as a multi-class problem taking into account various features such as actual time, driver trip grids, driver average daily trips, origin/destination trip degrees, origin/destination land use, etc. Considering that our dataset is imbalanced due to significantly fewer detour trips than normal driving trips, a combined model of hybrid sampling and ensemble learning is used to predict detour choices at the beginning of the trip. Results show that the proposed method is useful and powerful in the prediction of detour behavior. This paper is a quantitative study to empirically reveal the factors influencing different detour patterns and to perform ex ante predictions of detour choices, which facilitates managers to understand detour behavior and develop appropriate interventions.