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Uncovering Distribution Patterns of High Performance Taxis from Big Trace Data

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
School of Geosciences and Info-physics, Central South University, Changsha 410083, China
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(5), 134;
Received: 15 January 2017 / Revised: 19 April 2017 / Accepted: 25 April 2017 / Published: 28 April 2017
PDF [4542 KB, uploaded 28 April 2017]


The unbalanced distribution of taxi passengers in space and time affects taxi driver performance. Existing research has studied taxi driver performance by analyzing taxi driver strategies when the taxi is occupied. However, searching for passengers when vacant is costly for drivers, and it limits operational efficiency and income. Few researchers have taken the costs during vacant status into consideration when evaluating taxi driver performance. In this paper, we quantify taxi driver performance using the taxi’s average efficiency. We propose the concept of a high-efficiency single taxi trip and then develop a quantification and evaluation model for taxi driver performance based on single trip efficiency. In a case study, we first divide taxi drivers into top drivers and ordinary drivers, according to their performance as calculated from their GPS traces over a week, and analyze the space-time distribution and operating patterns of the top drivers. Then, we compare the space-time distribution of top drivers to ordinary drivers. The results show that top drivers usually operate far away from downtown areas, and the distribution of top driver operations is highly correlated with traffic conditions. We compare the proposed performance-based method with three other approaches to taxi operation evaluation. The results demonstrate the accuracy and feasibility of the proposed method in evaluating taxi driver performance and ranking taxi drivers. This paper could provide empirical insights for improving taxi driver performance. View Full-Text
Keywords: taxi performance; taxi efficiency; GPS big data; space-time distribution; data mining taxi performance; taxi efficiency; GPS big data; space-time distribution; data mining

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Tang, L.; Sun, F.; Kan, Z.; Ren, C.; Cheng, L. Uncovering Distribution Patterns of High Performance Taxis from Big Trace Data. ISPRS Int. J. Geo-Inf. 2017, 6, 134.

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