Abstract

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

Highlights

  • As a new type of technology, the intelligent transportation system combines theories of the sensor, data communication, data processing technology, artificial intelligence and automatic control [1,2].The Intelligent Transportation System (ITS) has become a research hotspot all over the world

  • For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the improved bird swarm optimization extreme learning machine (IBSAELM) model are smaller than the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM) and bird swarm optimization extreme learning machine (BSAELM) models, respectively

  • The improved bird swarm optimizer (IBSA) optimizer can realize extreme learning machine (ELM) parameters optimization, but can be applied to various fields; (2) This study proposes the IBSAELM model to forecast short-term traffic flow, and test experiments show that the IBSAELM

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Summary

Introduction

As a new type of technology, the intelligent transportation system combines theories of the sensor, data communication, data processing technology, artificial intelligence and automatic control [1,2]. The Intelligent Transportation System (ITS) has become a research hotspot all over the world. Japan is vigorously developing smart cars and autonomous driving technologies, and wants to build a world-class transportation system by 2020. In 2012, the number of intelligent transportation projects in China exceeded 230, with a total investment of more than 10 million yuan. With the increase of time, the number of intelligent transportation projects and total investments are increasing [3]. Short-term traffic flow forecasting is the basic content of a traffic intelligence system

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