On Predicting the Taxi-Passenger Demand: A Real-Time Approach

被引：0

作者：

Moreira-Matias, Luis ^{[1
,2
,3
]}

Gama, Joao ^{[2
,4
]}

Ferreira, Michel ^{[1
,5
]}

Mendes-Moreira, Joao ^{[2
,3
]}

Damas, Luis ^{[6
]}

机构：

[1] Inst Telecomunicoes, P-4200465 Oporto, Portugal

[2] INESC TEC, LIAAD, P-4200465 Oporto, Portugal

[3] Univ Porto, Fac Engn, Dept Informat Engn, P-4200465 Porto, Portugal

[4] Univ Porto, Fac Econ, P-4200465 Porto, Portugal

[5] Univ Porto, Fac Ciencia, Dep Ciencia Computadores, P-4169007 Porto, Portugal

[6] Geolink Lda, P-4050275 Porto, Portugal

来源：

PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2013 | 2013年 / 8154卷

关键词：

taxi-passenger demand; online learning; data streams; GPS data; auto-regressive integrated moving average (ARIMA); perceptron;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Informed driving is becoming a key feature to increase the sustainability of taxi companies. Some recent works are exploring the data broadcasted by each vehicle to provide live information for decision making. In this paper, we propose a method to employ a learning model based on historical GPS data in a real-time environment. Our goal is to predict the spatiotemporal distribution of the Taxi-Passenger demand in a short time horizon. We did so by using learning concepts originally proposed to a well-known online algorithm: the perceptron [1]. The results were promising: we accomplished a satisfactory performance to output the next prediction using a short amount of resources.

引用

页码：54 / 65

页数：12

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