This paper addresses the problem of predicting the express local train choices of metro passengers. The model was built and tested on the preferences observed from smart card data. The revealed preference data, because of intensiveness, can also accurately capture the marginal effects of the core attributes, in-vehicle time and wait time, on the express train choices by metro passengers. To be specific, the marginal disutility of a path decreases in in-vehicle time and increases in wait time. Accordingly, this paper employs a Box–Cox transform to adjust the constant marginality of a linear model to the nonconstant marginal disutility. The resulting nonlinear logit model improved the predictability of a conventional linear model. Tested on the Incheon–Yongsan interval of the Gyeong-In Line of the Seoul metropolitan area in South Korea, the model predicted a correct choice by a passenger in 99.9% and 99.5% of the cases during peak and nonpeak hour periods, respectively, compared with 96.9% and 95.8%, respectively, from a linear model. The model, applied to Line 9 without a parameter tuning, achieved a predictability greater than 95%.
The newly developed smartphone application, named RINO, in this study allows measuring absolute dynamic displacements and processing them in real time using state-of-the-art smartphone technologies, such as highperformance graphics processing unit (GPU), in addition to already powerful CPU and memories, embedded highspeed/resolution camera, and open-source computer vision libraries. A carefully designed color-patterned target and user-adjustable crop filter enable accurate and fast image processing, allowing up to 240fps for complete displacement calculation and real-time display. The performances of the developed smartphone application are experimentally validated, showing comparable accuracy with those of conventional laser displacement sensor.
This paper introduces Dr.METRO, which is a demand responsive metro-train operation planning program. It involves several key functions for metrotrain operation planning such as the data handling of passenger traffic, demand forecasting, train scheduling and the sequencing of a train-set operation.It uses mathematical optimization techniques to solve the train scheduling problems and heuristic algorithms for the sequencing of a train-set out of the train schedule. Besides the optimization technique-based algorithms, it fulfils several useful graphic user interface (GUI) functions that are designed to be user friendly.Dr.METRO is a software program developed to operate stand-alone with a compact and open structure. Its operating condition is IBM -a compatible PC and Windows framework. One of its merits is that a planner can use it separately from a formal business process in a company. So that he/she can prepare a train schedule according to his/her own creative concept and experience, not being restricted to organizational considerations. Dr.METRO helps them perform various quantitative analyses on schedule, sequence and monitor passenger traffic for better a train schedule and train-set sequence.
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