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An approach to merging railway ticket records into origin-destination pairs based on sparse matrix characteristic
LIN Ruixi 1 #,LIN Boliang 2 *
1.Department of Electronics and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, 999077
2.School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
*Correspondence author
#Submitted by
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Funding: none
Opened online:25 August 2014
Accepted by: none
Citation: LIN Ruixi,LIN Boliang.An approach to merging railway ticket records into origin-destination pairs based on sparse matrix characteristic[OL]. [25 August 2014] http://en.paper.edu.cn/en_releasepaper/content/4606634
 
 
This work describes a data merging method under the background of railway tickets data integration. Origin-destination (OD) pairs are fundamental data in transportation engineering, and are widely used in various applications. For example, predictive OD pairs of the long-term future are the basis of optimizing transportation network design. Optimization of transportation plans also relies on existing OD pairs. The size of an OD matrix is always huge in a real transportation system. Each element of the matrix, i.e. an OD pair, is accumulated by numerous passenger tickets or freight invoices. The size of an OD matrix is huge in real transportation systems. In a railway system, each element of the matrix, i.e. an OD pair, is accumulated by numerous passenger ticket or freight invoices. In 2013, China railway system transported 2.075 billion passengers. In other words, an equivalent number of tickets were sold. Each ticket consists of information on the name of origin and destination, train number, and seat class etc. Similarly, a railway freight invoice consists of information on the name of origin and destination, freight category (e.g. coal, oil, grain, and ore), and volume etc. Obviously, the tickets usually contain some identical fields of information. For example, two tickets can share the same origin and destination. Researchers use only particular fields of the tickets for different analysis purposes. Consequently, an effective approach is needed to merge the ticket records based on interested keywords and creates the corresponding compressed OD matrix. A simple direct merging method costs a lot of computations. This work proposes an effective and efficient approach, named origin-associated merging, to merge ticket records based on the sparse matrix characteristic. For experiments, 30 samples are created according to the characteristic of China railway passenger flow, ranging from 200 thousands to 6 million records with a step of 200 thousands records. The experimental results show that the time using origin-associated merging is about 1% of that of direct merging. It is worth mentioning that the number of passenger tickets in China railway system is approximately 6 million per day.
Keywords:data integration; rail transportation; OD pair; sparse matrix; ticket records
 
 
 

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