A study on identifying representative trips for mobility service design

Recently, with growing interest in urban mobility patterns, the demand for collecting and analysing origin-destination (OD) data is increasing. Due to the large scale and dimensionality of OD data, there are two issues in analysing the data: big-data storage and major pattern extraction. To deal with two issues at the same time, this study suggests a principal control analysis-based major demand identification method to improve the usability of microscopic OD data. Especially, this study focuses on finding principal components that preserve major patterns from OD data with small random noise so that the data can be effectively used for mobility service design. The proposed method is applied to smart card data of Seoul and Sejong and extracted major demand patterns from peak- and non-peak hour data of these cities. The degree of daily regularity, reconstruction accuracy, and compression rate of the reconstructed data is analysed varying sets of principal components. The obtained results show that the major demands contain a low volume and a large volume of demand and with lower-order principal components, major demands can be efficiently extracted by removing randomly appearing small-volume demand. In addition, the trade-off behaviour is observed between the degree of daily regularity and reconstruction accuracy depending on the compression rate. Based on the observations, it can be found that the loss of major demand patterns could be prevented when targeting a reconstruction accuracy of 90–95% and the proposed method can reduce the data size while preserving major mobility patterns.