Showing papers by "Xiaolei Ma published in 2012"

Transit smart card data mining for passenger origin information extraction

Abstract: The automated fare collection (AFC) system, also known as the transit smart card (SC) system, has gained more and more popularity among transit agencies worldwide. Compared with the conventional manual fare collection system, an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival. Although it is possible to collect highly valuable data from transit SC transactions, substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection. This is true especially for the Beijing AFC system, where a passenger’s boarding stop (origin) on a flat-rate bus is not recorded on the check-in scan. To extract passengers’ origin data from recorded SC transaction information, a Markov chain based Bayesian decision tree algorithm is developed in this study. Using the time invariance property of the Markov chain, the algorithm is further optimized and simplified to have a linear computational complexity. This algorithm is verified with transit vehicles equipped with global positioning system (GPS) data loggers. Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers’ origin information from SC transactions with a relatively high accuracy. Such transit origin data are highly valuable for transit system planning and route optimization.

Location optimization of multiple distribution centers under fuzzy environment

Abstract: Locating distribution centers optimally is a crucial and systematic task for decision-makers. Optimally located distribution centers can significantly improve the logistics system’s efficiency and reduce its operational costs. However, it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution center. With growing logistics demands, multiple distribution centers become necessary to meet customers’ requirements, but few studies have tackled the multiple distribution center locations (MDCLs) problem. This paper presents a comprehensive algorithm to address the MDCLs problem. Fuzzy integration and clustering approach using the improved axiomatic fuzzy set (AFS) theory is developed for location clustering based on multiple hierarchical evaluation criteria. Then, technique for order preference by similarity to ideal solution (TOPSIS) is applied for evaluating and selecting the best candidate for each cluster. Sensitivity analysis is also conducted to assess the influence of each criterion in the location planning decision procedure. Results from a case study in Guiyang, China, reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs selection. This new method may easily be extended to address location planning of other types of facilities, including hospitals, fire stations and schools.