2006Journal of Intelligent Transportation SystemsRequires access

Bus Arrival Time Prediction Using Support Vector Machines

Bin Yu, Yang Zhongzhen, Yao Baozhen

Open publisher page 269 citations

Abstract

Effective prediction of bus arrival time is central to many advanced traveler information systems. This article presents support vector machines (SVM), a new neural network algorithm, to predict bus arrival time. The objective of this paper is to examine the feasibility and applicability of SVM in vehicle travel time forecasting area. Segment, the travel time of current segment, and the latest travel time of next segment are taken as three input features. Bus arrival time predicted by the SVM is assessed with the data of transit route number 4 in Dalian economic and technological development zone in China and conclusions are drawn.

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What this paper is about

Effective prediction of bus arrival time is central to many advanced traveler information systems. This article presents support vector machines (SVM), a new neural network algorithm, to predict bus arrival time. The objective of this paper is to examine the feasibility and applicability of SVM in vehicle travel time forecasting area. Segment, the travel time of current segment, and the latest travel time of next segment are taken as three input features. Bus arrival time predicted by the SVM is assessed with the data of transit route number 4 in Dalian economic and technological development zone in China and conclusions are drawn.

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OpenAlex reports 269 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Effective prediction of bus arrival time is central to many advanced traveler information systems. This article presents support vector machines (SVM), a new neural network algorithm, to predict bus arrival time. The objective of this paper is to examine the feasibility and applicability of SVM in vehicle travel time forecasting area. Segment, the travel time of current segment, and the latest travel time of next segment are taken as three input features. Bus arrival time predicted by the SVM is assessed with the data of transit route number 4 in Dalian economic and technological development zone in China and conclusions are drawn.

Key concepts: Arrival time, Support vector machine, Travel time, Artificial neural network, Computer science, Real-time computing, Real-time data, Data mining

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