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An urban rail transit (URT) system is operated according to relatively punctual schedule, which is one of the most important constraints for a URT passenger's travel. Thus, it is the key to estimate passengers' train choices based on which passenger route choices as well as flow distribution on the URT network can be deduced. In this paper we propose a methodology that can estimate individual passenger's train choices with real timetable and automatic fare collection (AFC) data. First, we formulate the addressed problem using Manski's paradigm on modelling choice. Then, an integrated framework for estimating individual passenger's train choices is developed through a data-driven approach. The approach links each passenger trip to the most feasible train itinerary. Initial case study on Shanghai metro shows that the proposed approach works well and can be further used for deducing other important operational indicators like route choices, passenger flows on section, load factor of train, and so forth.

This paper first analyzes the causality of the maintenance and support (M&S) cost's generation; then draws the stock and flow diagram, establishes the correlation equation of stock, flow, instrumental variables, constants and determines their initial value; on the basis of the model reasonable, analyzes the model's calculation results, observes the change of the model results by adjusting model's parameters to determine the optimal strategy provides decision support for decision-makers. Emphatically analyzes the influence of parts' failure rate and preventive maintenance rate on maintenance costs during the equipment's life circle. The analysis results have verified this paper's simulation method's practicality.

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