A Short-Term Combination Forecasting Model for Traffic Flow Based on the BP Neural Network

Tie Xin Cheng; Wen Bin Du; Jing Zhu Chen

doi:10.4028/www.scientific.net/AMM.253-255.1339

Paper Titles

The POI of Highway Transportation and its Visual Search
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Optimal Prediction for Braking Speed of Urban Rail Vehicle
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The Research on Electric Bus Operation Intelligence Scheduling Model and Algorithm
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The Study of Traffic Simulation System Based on B/S Pattern
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A Short-Term Combination Forecasting Model for Traffic Flow Based on the BP Neural Network
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Study of Travel Mode Choice Behavior Based on Nested Logit Model
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Lane-Based Road Network Model and its Application in Simulation Road Network Modeling
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Optimization of Vehicle Routing Problem Based on Multi-Objective Genetic Algorithm
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Research of Vehicle Scheduling Problem and Algorithm Based on Emergency Needs
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 253-255A Short-Term Combination Forecasting Model for...

A Short-Term Combination Forecasting Model for Traffic Flow Based on the BP Neural Network

Abstract:

The forecasting for short-term traffic flow has always been one important and difficult research focus in the traffic forecasting areas. Based on the BP Neural Network, which was applied to nonlinear problems, the independent short-term forecasting models for the different traffic flow of the continuous time point series in one day and the constant date series at same time point were set up respectively, then, a short-term combination forecasting model for traffic flow, in which the regular fluctuations in the traffic flow data of the continuous time point series in one day and the constant date series at same time point were fully considered, was established, and can be applied to the complex spatio-temporal features of short-term traffic flow. With the sample of traffic flow dada, the forecasting results of the different models showed that the combination forecasting model provided a better forecast accuracy than the independent models.