Evaluating rail transit timetable using big passengers' data

Abstract

The unpredicted and unbalanced nature of passenger flow over temporal and spatial complicates the design and optimization of scheduled timetables. This study presents a model based on time-driven microscopic simulation to evaluate train timetable from the viewpoint of big passengers' data on rail transit lines. By calculating the load factor of rail lines for different times and sections, the proposed model assesses the drawbacks of scheduled timetables. It incorporates the number of alighting and boarding passengers, the train load factor, the number of passengers waiting for trains due to overcrowding in vehicles and the number of waiting passengers on the platform into the design. This methodology is examined in a real rail transit line that involves 634 trains and more than 1 million passenger trips. Experimental results demonstrate the efficiency of the proposed model.