Abstract
This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.
Highlights
The success of ITS technology is heavily dependent on the availability of timely and accurate estimates of prevailing and emerging traffic conditions
The research goals are as follows: (a) To propose an multidimensional scaling- (MDS-)based data dimension reduction method that can be used to conduct spatial-temporal correlativity analysis of urban traffic data and divide large amount of traffic flow data into smaller groups according to the level of similarity. (b) To demonstrate that the proposed method can be combined with existing prediction models
The accuracy of the prediction result using the small data set will be evaluated against those obtained using other data sets. (c) To illustrate that the proposed method can be combined with different kinds of prediction models and that the combined methods can be adapted to different traffic environments in urban road networks
Summary
The success of ITS technology is heavily dependent on the availability of timely and accurate estimates of prevailing and emerging traffic conditions. The main purpose of this study is to combine spatial-temporal correlativity analysis and existing prediction models and to test whether the combined method can reach a better prediction result. Short-term traffic prediction models have been developed for more than half a century Their application to urban traffic is limited. With increasing vehicle ownership and network complexity, there are increasing challenges in short-term traffic prediction for urban traffic. The proposed data dimension reduction method makes it possible to use less data to represent a whole data set, resulting in improved prediction accuracy. The research goals are as follows: (a) To propose an MDS-based data dimension reduction method that can be used to conduct spatial-temporal correlativity analysis of urban traffic data and divide large amount of traffic flow data into smaller groups according to the level of similarity. The accuracy of the prediction result using the small data set will be evaluated against those obtained using other data sets. (c) To illustrate that the proposed method can be combined with different kinds of prediction models and that the combined methods can be adapted to different traffic environments in urban road networks
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