Understanding detour behavior in taxi services: A combined approach. (December 2022)

• Record Type: Journal Article
• Authors: Feng, Xiaoyan; Sun, Huijun; Wu, Jianjun; Lv, Ying; Zhi, Danyue
• Is Part Of: Transportation research. Volume 145(2022)
• Publication Date: 2022-12
• Subjects: Taxi travel -- Detour patterns -- Influence factor -- Imbalanced -- Combined model; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103950
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Present a general approach for identifying different detour patterns. Explore the factors affecting detour patterns from different aspects. Different detour patterns have different features and show universality in different cities. Predict drivers' detour choices in advance. Develop a combined model to solve the imbalanced multi-class prediction problem. Abstract: 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.